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"(0|[1-9][0-9]*)\\.(0|[1-9][0-9]*)\\.(0|[1-9][0-9]*)(?:-((?:0|[1-9][0-9]*|[0-9]*[a-zA-Z-][0-9a-zA-Z-]*)(?:\\.(?:0|[1-9][0-9]*|[0-9]*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\\+([0-9a-zA-Z-]+(?:\\.[0-9a-zA-Z-]+)*))?" + }, + "Pattern": { + "type": "string", + "regex": true + }, + "SchemaType": { + "type": "string", + "enum": [ + "RS0001", + "RS0002", + "RS0003", + "RS0004", + "RS0005", + "RS0006", + "RS0007" + ], + "descriptions": [ + "Liquid-Cooled Chiller", + "Unitary Cooling Air-Conditioning Equipment", + "Fan Assembly", + "Air-to-Air Direct Expansion Refrigerant System", + "Motor", + "Electronic Motor Drive", + "Mechanical Drive" + ] + }, + "CompressorType": { + "type": "string", + "enum": [ + "RECIPROCATING", + "SCREW", + "CENTRIFUGAL", + "ROTARY", + "SCROLL" + ], + "enum_text": [ + "Reciprocating", + "Screw", + "Centrifugal", + "Rotary", + "Scroll" + ], + "descriptions": [ + "Reciprocating compressor", + "Screw compressor", + "Centrifugal compressor", + "Rotary compressor", + "Scroll compressor" + ] + }, + "SpeedControlType": { + "type": "string", + "enum": [ + "DISCRETE", + "CONTINUOUS" + ], + "enum_text": [ + "Discrete", + "Continuous" + ], + "descriptions": [ + "Loading is controlled by cycling between one or more discrete stages", + "Loading is controlled by continuously varying the speed" + ] + }, + "CondenserType": { + "type": "string", + "enum": [ + "AIR", + "LIQUID", + "EVAPORATIVE" + ], + "enum_text": [ + "Air", + "Liquid", + "Evaporative" + ], + "descriptions": [ + "Air-cooled condenser", + "Liquid-cooled condenser", + "Evaporative condenser" + ] + }, + "LiquidConstituent": { + "type": "string", + "enum": [ + "WATER", + "PROPYLENE_GLYCOL", + "ETHYLENE_GLYCOL", + "SODIUM_CHLORIDE", + "CALCIUM_CHLORIDE", + "ETHANOL", + "METHANOL" + ], + "enum_text": [ + "Water", + "Propylene Glycol", + "Ethylene Glycol", + "Sodium Chloride", + "Calcium Chloride", + "Ethanol", + "Methanol" + ], + "descriptions": [ + "Water", + "Propylene glycol", + "Ethylene glycol", + "Sodium chloride", + "Calcium chloride", + "Ethanol", + "Methanol" + ] + }, + "ConcentrationType": { + "type": "string", + "enum": [ + "BY_VOLUME", + "BY_MASS" + ], + "enum_text": [ + "By Volume", + "By Mass" + ], + "descriptions": [ + "Concentration is defined as a fraction of total liquid mixture volume", + "Concentration is defined as a fraction of total liquid mixture mass" + ] + }, + "OperationState": { + "type": "string", + "enum": [ + "NORMAL", + "STANDBY" + ], + "enum_text": [ + "Normal", + "Standby" + ], + "descriptions": [ + "Indicates that the equipment is in normal operating state", + "Indicates that the equipment is in standby operating state" + ] + }, + "Metadata": { + "type": "object", + "properties": { + "data_model": { + "description": "Data model name", + "type": "string", + "pattern": "ASHRAE_205", + "notes": "Identifies the data model where the schema is defined", + "requiredText": "True", + "constraintsText": "\"ASHRAE_205\"" + }, + "schema": { + "description": "Schema name or identifier", + "$ref": "ASHRAE205.schema.json#/definitions/SchemaType", + "notes": "Identifies the schema used to define the data content", + "requiredText": "True" + }, + "schema_version": { + "description": "The version of the schema the data complies with", + "$ref": "ASHRAE205.schema.json#/definitions/Version", + "requiredText": "True" + }, + "id": { + "description": "Unique equipment identifier", + "$ref": "ASHRAE205.schema.json#/definitions/UUID", + "notes": [ + "Assigned by data publisher to identify the contained data", + "`id` shall remain unchanged for revised data" + ], + "requiredText": "True" + }, + "description": { + "description": "Description of data (suitable for display)", + "type": "string", + "requiredText": "True" + }, + "data_timestamp": { + "description": "Date of publication", + "$ref": "ASHRAE205.schema.json#/definitions/Timestamp", + "notes": "Date/time of publication of the data", + "requiredText": "True" + }, + "data_version": { + "description": "Integer version identifier for the data in the representation", + "type": "integer", + "minimum": 1, + "notes": [ + "Used by data publisher to track revisions of the data for a specific representation", + "`data_version` shall be incremented for each data revision" + ], + "requiredText": "True", + "constraintsText": ">=1" + }, + "data_source": { + "description": "Source(s) of the data", + "type": "string", + "notes": [ + "Used by data publisher to document methods (e.g., software and version) used to generate data", + "***Informative note:*** `data_source` may be different from other data source(s) included elsewhere within the data" + ] + }, + "disclaimer": { + "description": "Characterization of accuracy, limitations, and applicability of this data", + "type": "string" + }, + "notes": { + "description": "Additional Information", + "type": "string" + } + }, + "required": [ + "data_model", + "schema", + "schema_version", + "id", + "description", + "data_timestamp", + "data_version" + ], + "additionalProperties": false + }, + "LiquidMixture": { + "type": "object", + "properties": { + "liquid_components": { + "description": "An array of all liquid components within the liquid mixture", + "type": "array", + "items": { + "$ref": "ASHRAE205.schema.json#/definitions/LiquidComponent" + }, + "notes": "Array may contain a single component", + "requiredText": "True" + }, + "concentration_type": { + "description": "Defines whether concentration is defined on a volume or mass basis", + "$ref": "ASHRAE205.schema.json#/definitions/ConcentrationType", + "requiredText": "True" + } + }, + "required": [ + "liquid_components", + "concentration_type" + ], + "additionalProperties": false + }, + "LiquidComponent": { + "type": "object", + "properties": { + "liquid_constituent": { + "description": "Substance of this component of the mixture", + "$ref": "ASHRAE205.schema.json#/definitions/LiquidConstituent", + "requiredText": "True" + }, + "concentration": { + "description": "Concentration of this component of the mixture", + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + "notes": "If omitted, assume equal distribution with any other component with no defined concentration (e.g., can be left blank for the primary component)", + "constraintsText": [ + ">=0.0", + "<=1.0" + ] + } + }, + "required": [ + "liquid_constituent" + ], + "additionalProperties": false + }, + "Scaling": { + "type": "object", + "properties": { + "minimum": { + "description": "Minimum scaling factor", + "type": "number", + "exclusiveMinimum": 0.0, + "maximum": 1.0, + "units": "-", + "notes": "If not specified, may be scaled down to any value greater than zero", + "constraintsText": [ + ">0.0", + "<=1.0" + ] + }, + "maximum": { + "description": "Maximum scaling factor", + "type": "number", + "minimum": 1.0, + "units": "-", + "notes": "If not specified, unlimited", + "constraintsText": [ + ">=1.0" + ] + } + }, + "additionalProperties": false + } + }, + "version": "2.0.0" +} \ No newline at end of file diff --git a/applications/ashrae205/DX-Constant-Efficiency.RS0004.a205.json b/applications/ashrae205/DX-Constant-Efficiency.RS0004.a205.json new file mode 100644 index 00000000..e5c73265 --- /dev/null +++ b/applications/ashrae205/DX-Constant-Efficiency.RS0004.a205.json @@ -0,0 +1,343 @@ +{ + "metadata": { + "schema_version": "2.0.0", + "data_model": "ASHRAE_205", + "schema": "RS0004", + "description": "DX System with Constant Efficiency", + "id": "123e4567-e89b-12d3-a456-426614174000", + "data_timestamp": "2020-05-07T00:00Z", + "data_version": 1, + "disclaimer": "Example data not to be used for simulation", + "notes": "An example representation of the ASHRAE 90.1 Appendix G minimum requirements for unitary air-conditioners" + }, + "description": { + "product_information": { + "outdoor_unit_manufacturer": "ABC HVAC", + "outdoor_unit_model_number": "DX AB12", + "indoor_unit_manufacturer": "ABC HVAC", + "indoor_unit_model_number": "DX AB12", + "refrigerant": "R-410A", + "compressor_type": "SCROLL" + } + }, + "performance": { + "compressor_speed_control_type": "DISCRETE", + "cycling_degradation_coefficient": 0.25, + "performance_map_cooling": { + "grid_variables": { + "outdoor_coil_entering_dry_bulb_temperature": [ + 283.15, + 293.15, + 303.15, + 313.15, + 323.15 + ], + "indoor_coil_entering_relative_humidity": [ + 0, + 0.1, + 0.5, + 0.9, + 1 + ], + "indoor_coil_entering_dry_bulb_temperature": [ + 283.15, + 293.15, + 303.15, + 313.15 + ], + "indoor_coil_air_mass_flow_rate": [ + 5.4, + 26.8 + ], + "compressor_sequence_number": [ + 1, + 2 + ], + "ambient_absolute_air_pressure": [ + 81.273, + 101.325 + ] + }, + "lookup_variables": { + "gross_total_capacity": [ + 42191.636, + 42191.636, + 56877.119999999995, + 56877.119999999995, + 62919.299, + 62919.299, + 102901.802, + 102901.802, + 42191.636, + 42191.636, + 56877.119999999995, + 56877.119999999995, + 62919.299, + 62919.299, + 102901.802, + 102901.802, + 42191.636, + 42191.636, + 56877.119999999995, + 56877.119999999995, + 62919.299, + 62919.299, + 102901.802, + 102901.802, + 42191.636, + 42191.636, + 56877.119999999995, + 56877.119999999995, + 62919.299, + 62919.299, + 102901.802, + 102901.802, + 30500.344, + 30500.344, + 44039.92199999999, + 44039.92199999999, + 39370.92199999999, + 39370.92199999999, + 66637.795, + 66637.795, + 30500.344, + 30500.344, + 44039.92199999999, + 44039.92199999999, + 39370.92199999999, + 39370.92199999999, + 66637.795, + 66637.795, + 30500.344, + 30500.344, + 44039.92199999999, + 44039.92199999999, + 39370.92199999999, + 39370.92199999999, + 66637.795, + 66637.795, + 30500.344, + 30500.344, + 44039.92199999999, + 44039.92199999999, + 39370.92199999999, + 39370.92199999999, + 66637.795, + 66637.795 + ], + "gross_sensible_capacity": [ + 25314.9816, + 25314.9816, + 34126.272, + 34126.272, + 37751.579399999995, + 37751.579399999995, + 61741.08119999999, + 61741.08119999999, + 25314.9816, + 25314.9816, + 34126.272, + 34126.272, + 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39982.676999999996 + ], + "gross_power": [ + 13610.205161290321, + 13610.205161290321, + 18347.458064516126, + 18347.458064516126, + 20296.54806451613, + 20296.54806451613, + 33194.129677419354, + 33194.129677419354, + 13610.205161290321, + 13610.205161290321, + 18347.458064516126, + 18347.458064516126, + 20296.54806451613, + 20296.54806451613, + 33194.129677419354, + 33194.129677419354, + 13610.205161290321, + 13610.205161290321, + 18347.458064516126, + 18347.458064516126, + 20296.54806451613, + 20296.54806451613, + 33194.129677419354, + 33194.129677419354, + 13610.205161290321, + 13610.205161290321, + 18347.458064516126, + 18347.458064516126, + 20296.54806451613, + 20296.54806451613, + 33194.129677419354, + 33194.129677419354, + 9838.82064516129, + 9838.82064516129, + 14206.4264516129, + 14206.4264516129, + 12700.297419354836, + 12700.297419354836, + 21496.062903225804, + 21496.062903225804, + 9838.82064516129, + 9838.82064516129, + 14206.4264516129, + 14206.4264516129, + 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+ "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL", + "NORMAL" + ] + } + }, + "performance_map_standby": { + "grid_variables": { + "outdoor_coil_environment_dry_bulb_temperature": [ + 283.15, + 323.15 + ] + }, + "lookup_variables": { + "gross_power": [ + 40, + 40 + ] + } + } + } +} \ No newline at end of file diff --git a/applications/ashrae205/README.md b/applications/ashrae205/README.md new file mode 100644 index 00000000..6273ba57 --- /dev/null +++ b/applications/ashrae205/README.md @@ -0,0 +1,153 @@ +# DX Performance Mapping Generator for STD 205 + +This project provides tools to generate and validate **ASHRAE Standard 205-compliant performance maps** for DX (Direct Expansion) air conditioning units using curve-based performance data. +The workflow is fully implemented in a single script: **`main.py`**. + +--- + +## ๐Ÿ“Œ Features + +* Generates performance curves using the **copper** library (ASHRAE 90.1 code-based EER/IEER requirements). +* Saves curves into individual **CSV files** (e.g., `AC_Perf_*.csv`) and an optional combined CSV. +* Computes cooling performance over a grid of conditions defined in a template JSON. +* Supports various curve types: **linear, quadratic, cubic, bi-quadratic**. +* Uses **PsychroLib** for accurate psychrometric calculations. +* Estimates sensible cooling capacity from total capacity (simplified SHR approach). +* Saves computed results into a structured JSON format compliant with ASHRAE Standard 205. +* Validates generated JSON against **RS0004** and **ASHRAE205** schemas. +* Converts JSON results to **Excel (XLSX)** format using [Toolkit 205](https://github.com/open205/toolkit-205). + +--- + +## ๐Ÿ“‚ File Structure + +* `main.py` + โ†’ Main script that performs the **entire workflow**: + 1. Generate performance curves (CSV) + 2. Populate STD205 JSON template with results + 3. Validate JSON against ASHRAE 205 schema + 4. Convert JSON โ†’ XLSX + +* `input/` + โ†’ JSON files for conversion (e.g., `AC_Perf_901_2022_65_to_135_11.55EER_14.8IEER.json`). + +* `xlsx/` + โ†’ Output folder where converted XLSX files will be saved. + +* `AC_Perf_*.csv` + โ†’ Example CSV file generated from the copper-based curve generation. + +* `DX-Constant-Efficiency.RS0004.a205.json` + โ†’ Template JSON defining the variable grid and structure for performance mapping. + +* `RS0004.schema.json`, `ASHRAE205.schema.json` + โ†’ Schemas used for validation. + +## ๐Ÿ“ฅ Requirements + +Install core dependencies: + +```bash +pip install pandas numpy matplotlib psychrolib jsonschema +```` + +For **JSON โ†’ XLSX conversion** and **schema validation**, you also need +[Toolkit 205](https://github.com/open205/toolkit-205). + +### Setup Toolkit 205 + +1. Clone the Toolkit 205 repository: + + ```bash + git clone --recurse-submodules https://github.com/open205/toolkit-205.git + ``` + +2. Copy its contents into the project: + + ```bash + cp -r toolkit-205/tk205 applications/ashrae205/ + cp -r toolkit-205/schema-205/schema205 applications/ashrae205/ + ``` + +3. Install Poetry (if not already installed): + + ```bash + pip install poetry + ``` + +4. Build schemas inside `applications/ashrae205/`: + + ```bash + cd applications/ashrae205/ + poetry install + poetry run doit build_schema + ``` + +This will generate the required schema files (**RS0004.schema.json** and **ASHRAE205.schema.json**) used for validation. + + +--- + +## ๐Ÿง  Workflow + +1. **Generate Curves** + `main.py` uses the **copper** library to generate code-compliant DX performance curves (EER/IEER). + Outputs: CSVs (`AC_Perf_*.csv`) saved in the working directory. + +2. **Map to JSON** + `main.py` reads a selected CSV (e.g., `AC_Perf_901_2022_65_to_135_11.55EER_14.8IEER.csv`), applies modifier curves, computes capacity & power, and fills the **STD205 JSON template**. + +3. **Validation** + The JSON output is validated against the **RS0004** and **ASHRAE205** schemas. + +4. **Conversion** (optional) + Using **tk205**, the script converts valid JSON into Excel `.xlsx` format (stored in `xlsx/`). + +5. **Plot** (optional) + Using matplotlab to plot the figures of curves and maps for comparisons. + +--- + +## โš™๏ธ Configuration + +Key parameters inside `main.py`: + +```python +csv_file = "AC_Perf_901_2022_65_to_135_11.55EER_14.8IEER.csv" # input curve CSV +json_template_file = "DX-Constant-Efficiency.RS0004.a205.json" +output_json_file = "input/DX_Updated_STD205_Output.json" + +nominal_capacity = 232057 # W +nominal_eer = 9.2 +nominal_SHR = 0.7 +``` + +--- + +## ๐Ÿงช Example Usage + +Run the full workflow: + +```bash +python main.py +``` + +Example outputs: + +``` +โœ… Individual CSVs written to ./ +โœ… Combined CSV written: ieer_specific_curves.csv +โœ… STD205 JSON updated and saved to: input/DX_Updated_STD205_Output.json +โœ… DX_Updated_STD205_Output.json is valid according to RS0004.schema.json +โœ… Converted JSON in input โ†’ XLSX in xlsx +๐Ÿ“ˆ Saved: ./ +``` + +--- + +## โš ๏ธ Notes + +* Script assumes **PLR = 1.0** (part-load ratio fixed). +* Sensible capacity estimation is simplified (constant SHR). +* Curve types must be one of: `bi_quad`, `cubic`, `quadratic`, `linear`. +* Validation requires **RS0004.schema.json** and **ASHRAE205.schema.json** in the working directory. diff --git a/applications/ashrae205/RS0004.schema.json b/applications/ashrae205/RS0004.schema.json new file mode 100644 index 00000000..53db298e --- /dev/null +++ b/applications/ashrae205/RS0004.schema.json @@ -0,0 +1,361 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Air-to-Air Direct Expansion Refrigerant System", + "description": "Schema for ASHRAE 205 annex RS0004: Air-to-Air Direct Expansion Refrigerant System", + "definitions": { + "RS0004": { + "type": "object", + "properties": { + "metadata": { + "description": "Metadata data group", + "$ref": "ASHRAE205.schema.json#/definitions/Metadata", + "requiredText": "True", + "constraintsText": "schema=RS0004" + }, + "description": { + "description": "Data group describing product and rating information", + "$ref": "RS0004.schema.json#/definitions/Description" + }, + "performance": { + "description": "Data group containing performance information", + "$ref": "RS0004.schema.json#/definitions/Performance", + "requiredText": "True" + } + }, + "required": [ + "metadata", + "performance" + ], + "additionalProperties": false + }, + "Description": { + "type": "object", + "properties": { + "product_information": { + "description": "Data group describing product information", + "$ref": "RS0004.schema.json#/definitions/ProductInformation" + } + }, + "additionalProperties": false + }, + "ProductInformation": { + "type": "object", + "properties": { + "outdoor_unit_manufacturer": { + "description": "Outdoor unit manufacturer name", + "type": "string" + }, + "outdoor_unit_model_number": { + "description": "Outdoor unit model number", + "$ref": "ASHRAE205.schema.json#/definitions/Pattern", + "notes": "Pattern shall match all model numbers that can be represented by the representation" + }, + "indoor_unit_manufacturer": { + "description": "Indoor unit manufacturer name", + "type": "string", + "notes": "May be omitted for packaged systems with a single manufacturer" + }, + "indoor_unit_model_number": { + "description": "Indoor unit model number", + "$ref": "ASHRAE205.schema.json#/definitions/Pattern", + "notes": "Pattern shall match all model numbers that can be represented by the representation" + }, + "refrigerant": { + "description": "Refrigerant used", + "type": "string", + "notes": "The string shall start with 'R-' and then include the refrigerant number designation conforming to ANSI/ASHRAE Standard 34[@ASHRAE34]" + }, + "compressor_type": { + "description": "Type of compressor", + "$ref": "ASHRAE205.schema.json#/definitions/CompressorType" + } + }, + "additionalProperties": false + }, + "Performance": { + "type": "object", + "properties": { + "compressor_speed_control_type": { + "description": "Method used to control different speeds of the compressor", + "$ref": "ASHRAE205.schema.json#/definitions/SpeedControlType", + "requiredText": "True" + }, + "cycling_degradation_coefficient": { + "description": "Cycling degradation coefficient (C~D~) as described in AHRI 210/240", + "type": "number", + "minimum": 0.0, + "exclusiveMaximum": 1.0, + "units": "-", + "notes": [ + "Used for the lowest stage when the unit cycles to meet load", + "***Informative note:*** 340/360 specifies a fixed cycling degradation coefficient of approximately 0.12" + ], + "requiredText": "True", + "constraintsText": [ + ">=0.0", + "<1.0" + ] + }, + "scaling": { + "description": "Specifies the range the performance data can be scaled to represent different capacity equipment", + "$ref": "ASHRAE205.schema.json#/definitions/Scaling", + "notes": "If not present, scaling of the performance data is not allowed" + }, + "performance_map_cooling": { + "description": "Data group describing cooling performance over a range of conditions", + "$ref": "RS0004.schema.json#/definitions/PerformanceMapCooling", + "requiredText": "True" + }, + "performance_map_standby": { + "description": "Data group describing standby performance", + "$ref": "RS0004.schema.json#/definitions/PerformanceMapStandby", + "requiredText": "True" + } + }, + "required": [ + "compressor_speed_control_type", + "cycling_degradation_coefficient", + "performance_map_cooling", + "performance_map_standby" + ], + "additionalProperties": false + }, + "PerformanceMapCooling": { + "type": "object", + "properties": { + "grid_variables": { + "description": "Data group defining the grid variables for cooling performance", + "$ref": "RS0004.schema.json#/definitions/GridVariablesCooling", + "requiredText": "True" + }, + "lookup_variables": { + "description": "Data group defining the lookup variables for cooling performance", + "$ref": "RS0004.schema.json#/definitions/LookupVariablesCooling", + "requiredText": "True" + } + }, + "required": [ + "grid_variables", + "lookup_variables" + ], + "additionalProperties": false + }, + "GridVariablesCooling": { + "type": "object", + "properties": { + "outdoor_coil_entering_dry_bulb_temperature": { + "description": "Dry bulb temperature of the air entering the outdoor coil", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "K", + "requiredText": "True", + "constraintsText": ">=0.0" + }, + "indoor_coil_entering_relative_humidity": { + "description": "Relative humidity of the air entering the indoor coil", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "-", + "notes": "As measured immediately before entering the coil (i.e., after the fan in a blow-through configuration)", + "requiredText": "True", + "constraintsText": [ + ">=0.0", + "<=1.0" + ] + }, + "indoor_coil_entering_dry_bulb_temperature": { + "description": "Dry bulb temperature of the air entering the indoor coil", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "K", + "notes": "As measured immediately before entering the coil (i.e., after the fan in a blow-through configuration)", + "requiredText": "True", + "constraintsText": ">=0.0" + }, + "indoor_coil_air_mass_flow_rate": { + "description": "Mass flow rate of air entering the indoor coil", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "kg/s", + "scalable": true, + "requiredText": "True", + "constraintsText": ">0.0" + }, + "compressor_sequence_number": { + "description": "Index indicating the relative capacity order of the compressor speed/stage expressed in order from lowest capacity (starting at 1) to highest capacity", + "type": "array", + "minItems": 1, + "items": { + "type": "integer" + }, + "units": "-", + "notes": [ + "If `compressor_speed_control_type` is `DISCRETE`, sequence numbers shall be provided for each discrete stage of the compressor(s)", + "If `compressor_speed_control_type` is `CONTINUOUS`, sufficient sequence numbers shall be provided to capture the continuous operation of the compressor(s)" + ], + "requiredText": "True", + "constraintsText": ">=1" + }, + "ambient_absolute_air_pressure": { + "description": "Ambient absolute air pressure", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "Pa", + "requiredText": "True", + "constraintsText": ">=0.0" + } + }, + "required": [ + "outdoor_coil_entering_dry_bulb_temperature", + "indoor_coil_entering_relative_humidity", + "indoor_coil_entering_dry_bulb_temperature", + "indoor_coil_air_mass_flow_rate", + "compressor_sequence_number", + "ambient_absolute_air_pressure" + ], + "additionalProperties": false + }, + "LookupVariablesCooling": { + "type": "object", + "properties": { + "gross_total_capacity": { + "description": "Total heat removed by the indoor coil", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "W", + "scalable": true, + "notes": "Shall not include fan heat", + "requiredText": "True", + "constraintsText": ">=0.0" + }, + "gross_sensible_capacity": { + "description": "Sensible heat removed by the indoor coil", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "W", + "scalable": true, + "notes": "Shall not include fan heat", + "requiredText": "True", + "constraintsText": ">=0.0" + }, + "gross_power": { + "description": "Gross power draw (of the outdoor unit)", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "W", + "scalable": true, + "notes": [ + "Includes compressor, outdoor fan, and any auxiliary power used by the unit's controls and any sump heater", + "Shall not include power drawn by the indoor fan" + ], + "requiredText": "True", + "constraintsText": ">0.0" + }, + "operation_state": { + "description": "The operation state at the operating conditions", + "type": "array", + "items": { + "$ref": "ASHRAE205.schema.json#/definitions/OperationState" + }, + "units": "-", + "requiredText": "True" + } + }, + "required": [ + "gross_total_capacity", + "gross_sensible_capacity", + "gross_power", + "operation_state" + ], + "additionalProperties": false + }, + "PerformanceMapStandby": { + "type": "object", + "properties": { + "grid_variables": { + "description": "Data group defining the grid variables for standby performance", + "$ref": "RS0004.schema.json#/definitions/GridVariablesStandby", + "requiredText": "True" + }, + "lookup_variables": { + "description": "Data group defining the lookup variables for standby performance", + "$ref": "RS0004.schema.json#/definitions/LookupVariablesStandby", + "requiredText": "True" + } + }, + "required": [ + "grid_variables", + "lookup_variables" + ], + "additionalProperties": false + }, + "GridVariablesStandby": { + "type": "object", + "properties": { + "outdoor_coil_environment_dry_bulb_temperature": { + "description": "Dry bulb temperature of the air in the environment of the outdoor coil", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "K", + "requiredText": "True", + "constraintsText": ">=0.0" + } + }, + "required": [ + "outdoor_coil_environment_dry_bulb_temperature" + ], + "additionalProperties": false + }, + "LookupVariablesStandby": { + "type": "object", + "properties": { + "gross_power": { + "description": "Gross power draw (of the outdoor unit)", + "type": "array", + "minItems": 1, + "items": { + "type": "number" + }, + "units": "W", + "scalable": true, + "notes": "Includes any auxiliary power used by the unit's controls and any sump heater", + "requiredText": "True", + "constraintsText": ">0.0" + } + }, + "required": [ + "gross_power" + ], + "additionalProperties": false + } + }, + "version": "2.0.0", + "$ref": "#/definitions/RS0004" +} \ No newline at end of file diff --git a/applications/ashrae205/input/AC_Perf_901_2022_135_to_240_10.91EER_14.2IEER.json b/applications/ashrae205/input/AC_Perf_901_2022_135_to_240_10.91EER_14.2IEER.json new file mode 100644 index 00000000..94091174 --- /dev/null +++ b/applications/ashrae205/input/AC_Perf_901_2022_135_to_240_10.91EER_14.2IEER.json @@ -0,0 +1,3287 @@ +{ + "metadata": { + "schema_version": "2.0.0", + "data_model": "ASHRAE_205", + "schema": "RS0004", + "description": "DX System with Constant Efficiency", + "id": "123e4567-e89b-12d3-a456-426614174000", + "data_timestamp": "2020-05-07T00:00Z", + "data_version": 1, + "disclaimer": "Example data not to be used for simulation", + "notes": "An example representation of the ASHRAE 90.1 Appendix G minimum requirements for unitary air-conditioners" + }, + "description": { + "product_information": { + "outdoor_unit_manufacturer": "ABC HVAC", + "outdoor_unit_model_number": "DX AB12", + "indoor_unit_manufacturer": "ABC HVAC", + "indoor_unit_model_number": "DX AB12", + "refrigerant": "R-410A", + "compressor_type": "SCROLL" + } + }, + "performance": { + "compressor_speed_control_type": "DISCRETE", + "cycling_degradation_coefficient": 0.25, + "performance_map_cooling": { + "grid_variables": { + 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a/applications/ashrae205/input/AC_Perf_901_2022_65_to_135_11.55EER_14.8IEER.json b/applications/ashrae205/input/AC_Perf_901_2022_65_to_135_11.55EER_14.8IEER.json new file mode 100644 index 00000000..806e9067 --- /dev/null +++ b/applications/ashrae205/input/AC_Perf_901_2022_65_to_135_11.55EER_14.8IEER.json @@ -0,0 +1,3287 @@ +{ + "metadata": { + "schema_version": "2.0.0", + "data_model": "ASHRAE_205", + "schema": "RS0004", + "description": "DX System with Constant Efficiency", + "id": "123e4567-e89b-12d3-a456-426614174000", + "data_timestamp": "2020-05-07T00:00Z", + "data_version": 1, + "disclaimer": "Example data not to be used for simulation", + "notes": "An example representation of the ASHRAE 90.1 Appendix G minimum requirements for unitary air-conditioners" + }, + "description": { + "product_information": { + "outdoor_unit_manufacturer": "ABC HVAC", + "outdoor_unit_model_number": "DX AB12", + "indoor_unit_manufacturer": "ABC HVAC", + "indoor_unit_model_number": "DX AB12", + 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"performance_map_standby": { + "grid_variables": { + "outdoor_coil_environment_dry_bulb_temperature": [ + 283.15, + 323.15 + ] + }, + "lookup_variables": { + "gross_power": [ + 40, + 40 + ] + } + } + } +} \ No newline at end of file diff --git a/applications/ashrae205/main.py b/applications/ashrae205/main.py new file mode 100644 index 00000000..7a00c16f --- /dev/null +++ b/applications/ashrae205/main.py @@ -0,0 +1,551 @@ +# ================================================ +# Unitary DX Performance Mapping Generator for STD 205 +# ================================================ +# 1. Generate performance curves (CSV) +# 2. Populate STD205 JSON template with curves +# 3. Validate JSON against ASHRAE 205 schema +# 4. Convert JSON to other formats using tk205 +# ================================================ + +import json +import itertools +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import psychrolib +from pathlib import Path +from jsonschema import Draft7Validator, RefResolver +import tk205 +import os +import glob +import sys +# ------------------------- +# STEP 1: Generate performance CSV +# ------------------------- +""" +Generate performance curve CSVs for unitary DX equipment using the `copper` library. +Replicates the Jupyter notebook workflow (no plots, no IDF export). +""" +copper_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")) +if copper_path not in sys.path: + sys.path.insert(0, copper_path) +import copper as cp + +random_seed = 1 + +capacities = { + "65_to_135": 96, # kBtu/h; 8 ton + "135_to_240": 180, # kBtu/h; 15 ton + "240_to_760": 480, # kBtu/h; 40 ton + "gt760": 792, # kBtu/h; 66 ton +} + +# See backup calcs workbooks in kW +fan_power = { + "65_to_135": 0.524, + "135_to_240": 1.197, + "240_to_760": 5.243, + "gt760": 11.190, +} + +requirements = { + "901_2004": { + "eer": { + "65_to_135": 10.3, + "135_to_240": 9.7, + "240_to_760": 9.5, + "gt760": 9.2, + }, + "ieer": { + "65_to_135": None, + "135_to_240": None, + "240_to_760": 9.7, + "gt760": 9.4, + } + }, + "901_2022": { + "eer": { + "65_to_135": None, + "135_to_240": None, + "240_to_760": None, + "gt760": None, + }, + "ieer": { + "65_to_135": 14.8, + "135_to_240": 14.2, + "240_to_760": 13.2, + "gt760": 12.5, + } + } +} + +for code in list(requirements.keys()): + for cap in list(capacities.keys()): + if cap not in requirements[code]["ieer"].keys(): + continue + + # Convert to ton + tonnage = cp.Units(value=capacities[cap], unit="kbtu/h").conversion(new_unit="ton") + + # Indoor fan speeds per your logic + if "2004" in code: + if cap == "gt760": + indoor_fan_speeds = 2 + else: + indoor_fan_speeds = 1 + else: + indoor_fan_speeds = 2 + + # Build equipment object + dx = cp.UnitaryDirectExpansion( + compressor_type="scroll", + condenser_type="air", + compressor_speed="constant", + ref_cap_unit="ton", + ref_net_cap=tonnage, + full_eff=requirements[code]["eer"][cap], + full_eff_unit="eer", + part_eff_ref_std="ahri_340/360", + indoor_fan_speeds=indoor_fan_speeds, + indoor_fan_power=fan_power[cap], + indoor_fan_power_unit="kW", + ) + + # Branching consistent with your approach + agg_only = False + eer_val = requirements[code]["eer"][cap] + ieer_val = requirements[code]["ieer"][cap] + + if eer_val is None: + # Compute EER from IEER + dx.part_eff = ieer_val + dx.full_eff = dx.ieer_to_eer(ieer_val) + name = f"AC_Perf_{code}_{cap}_{round(dx.full_eff,2)}EER_{round(dx.part_eff,2)}IEER" + + elif ieer_val is None: + # EER-only case + agg_only = True + name = f"AC_Perf_{code}_{cap}_{round(dx.full_eff,2)}EER" + + else: + # Both provided + dx.part_eff = ieer_val + name = f"AC_Perf_{code}_{cap}_{round(dx.full_eff,2)}EER_{round(dx.part_eff,2)}IEER" + + # Degradation & curve gen + if "2004" in code: + dx.degradation_coefficient = 0.25 + else: + dx.degradation_coefficient = 0.15 + dx.add_cycling_degradation_curve(overwrite=True) + + _ = dx.generate_set_of_curves( + method="nearest_neighbor", + tol=0.01, + num_nearest_neighbors=5, + verbose=False, + vars=["eir-f-t"], + random_seed=random_seed, + agg_only=agg_only + ) + + # Apply degradation again (matches original flow) + dx.add_cycling_degradation_curve(overwrite=True) + + # Prepare curve set & enforce limits + curves = cp.SetofCurves() + curves.curves = dx.set_of_curves + curves.eqp = dx + + limits = dx.get_ranges() + for c in curves.curves: + xs = limits[c.out_var]["vars_range"][0] + c.x_min = xs[0] + c.x_max = xs[1] + if len(limits[c.out_var]["vars_range"]) > 1: + ys = limits[c.out_var]["vars_range"][1] + c.y_min = ys[0] + c.y_max = ys[1] + if "ff" in c.out_var: + c.x_min = 0.4 + if "plf" in c.out_var: + c.out_min = 0.0 + + # Export each set to CSV (same naming) + curves.export(path="./", fmt="csv", name=name) + +# Combine into a single CSV (same header & approach as yours) +with open("./ieer_specific_curves.csv", "w") as nf: + nf.write("name,variable,unit_type,curve_type,min_x,max_x,min_y,max_y,coeff1,coeff2,coeff3,coeff4,coeff5,coeff6\n") + for f in glob.glob("./AC_Perf*.csv"): + with open(f, "r") as curve_set: + for line in curve_set: + nf.write(line) + + +# ------------------------- +# STEP 2: Populate STD205 JSON +# ------------------------- +# Set psychrometric unit system to SI +psychrolib.SetUnitSystem(psychrolib.SI) +# === CONFIGURATION === +csv_file = 'AC_Perf_901_2022_65_to_135_11.55EER_14.8IEER.csv' # example curve file +json_template_file = 'DX-Constant-Efficiency.RS0004.a205.json' +# Keep output under input/ so STEP 4 can convert it +output_json_file = 'input/DX_Updated_STD205_Output.json' + +# Nominal values (example uses gt760 default capacity) +nominal_capacity = 232057 # W, โ‰ˆ792 kBtu/h +nominal_eer = 9.2 # IP EER example +nominal_eir = 1.0 / max(nominal_eer, 1e-9) +nominal_SHR = 0.7 + +# === UTILITY FUNCTIONS === +def compute_wetbulb(Tdb_K, RH_frac, pressure_kPa): + """Compute wet-bulb temperature in Celsius.""" + Tdb_C = Tdb_K - 273.15 + pressure_Pa = pressure_kPa * 1000 + return psychrolib.GetTWetBulbFromRelHum(Tdb_C, RH_frac, pressure_Pa) + +def evaluate_curve(row, x1, x2=0.0): + ctype = str(row['CurveUse']).lower() + if ctype == 'bi_quad': + return (row['C0'] + row['C1'] * x1 + row['C2'] * x1**2 + + row['C3'] * x2 + row['C4'] * x2**2 + row['C5'] * x1 * x2) + elif ctype == 'cubic': + return row['C0'] + row['C1'] * x1 + row['C2'] * x1**2 + row['C3'] * x1**3 + elif ctype == 'quadratic': + return row['C0'] + row['C1'] * x1 + row['C2'] * x1**2 + elif ctype == 'linear': + return row['C0'] + row['C1'] * x1 + else: + raise ValueError(f"Unsupported curve type: {ctype}") + +def calculate_performance( + cap_f_t_row, cap_f_flow_row, eir_f_t_row, eir_f_flow_row, plf_f_plr_row, + x1, x2, flow_ratio, plr, nominal_capacity, nominal_eir +): + cap_f_t = evaluate_curve(cap_f_t_row, x1, x2) + cap_f_ff = evaluate_curve(cap_f_flow_row, flow_ratio) + eir_f_t = evaluate_curve(eir_f_t_row, x1, x2) + eir_f_ff = evaluate_curve(eir_f_flow_row, flow_ratio) + plf = max(evaluate_curve(plf_f_plr_row, plr), 1e-6) + + gross_capacity = nominal_capacity * cap_f_t * cap_f_ff + eir = nominal_eir * eir_f_t * eir_f_ff + power = gross_capacity * eir * (plr / plf) + return gross_capacity, 0.0, power # sensible is computed via ADP method below + +# --- colleague-style ADP finder (line to saturation), using PsychroLib --- +def _cp_moist_air_J_per_kgK(w): + return 1006.0 + 1860.0 * w + +def _dewpoint_from_w(w, P_Pa): + """ + Dewpoint [ยฐC] from humidity ratio w [kg/kg] and pressure P [Pa]. + Fixes the earlier error by providing both args to PsychroLib API. + """ + Pw = psychrolib.GetVapPresFromHumRatio(w, P_Pa) + # PsychroLib signature is GetTDewPointFromVapPres(TDryBulb, VapPres) + # TDryBulb is not used in the calculation; a non-negative filler is fine. + return psychrolib.GetTDewPointFromVapPres(0.0, Pw) + +def estimate_sensible_capacity_coolpropline( + Q_total_W, Tdbi_K, RH_frac, flow_rate_kg_s, pressure_kPa, SHR_rated=0.7 +): + """ + 1) From Q_total and SHR, derive (T_out, w_out). + 2) Fit line (T, w) through (T_in, w_in) and (T_out, w_out). + 3) March along that line to find ADP where T_dp(w_x) ~= T_x. + 4) Compute sensible/latent using T_out (keeps Q_total consistent). + """ + P_Pa = pressure_kPa * 1000.0 + T_in_C = Tdbi_K - 273.15 + RH = max(0.01, min(0.99, RH_frac)) + m_dot = max(flow_rate_kg_s, 1e-9) + + # Inlet state + w_in = psychrolib.GetHumRatioFromRelHum(T_in_C, RH, P_Pa) + h_in = psychrolib.GetMoistAirEnthalpy(T_in_C, w_in) + + # Outlet conditions from total capacity + SHR split + delta_h = Q_total_W / m_dot + h_out = h_in - delta_h + # Standard SHR split: keep T at inlet to solve w_out, then T_out from (h_out, w_out) + h_tin_wout = h_in - (1.0 - SHR_rated) * delta_h + w_out = psychrolib.GetHumRatioFromEnthalpyAndTDryBulb(h_tin_wout, T_in_C) + T_out_C = psychrolib.GetTDryBulbFromEnthalpyAndHumRatio(h_out, w_out) + + # Line in (T, w) + dT = T_out_C - T_in_C + if abs(dT) < 1e-9: + cp_air = _cp_moist_air_J_per_kgK(w_in) + Q_sens = m_dot * cp_air * (T_in_C - T_out_C) + Q_lat = Q_total_W - Q_sens + SHR_actual = Q_sens / max(Q_total_W, 1e-6) + return Q_sens, Q_lat, SHR_actual + + a = (w_out - w_in) / dT + b = w_in - a * T_in_C + + # March along line to find ADP (t_dp(w_x) ~ t_x) + t_x = T_out_C + 1e-3 + incr = 0.001 + for _ in range(2000): + t_x += incr + w_x = max(1e-8, a * t_x + b) + t_dp = _dewpoint_from_w(w_x, P_Pa) + err = t_dp - t_x + if abs(err) < 1e-4: + break + incr = err / 10.0 + + # Sensible/latent with T_out from step above + cp_air = _cp_moist_air_J_per_kgK(w_in) + Q_sens = m_dot * cp_air * (T_in_C - T_out_C) + Q_lat = Q_total_W - Q_sens + SHR_actual = Q_sens / max(Q_total_W, 1e-6) + return Q_sens, Q_lat, SHR_actual + +df_curves = pd.read_csv(csv_file, header=None) +df_curves.columns = [ + 'CurveName', 'CurveType', 'Unused', 'CurveUse', + 'X1Min', 'X1Max', 'X2Min', 'X2Max', + 'C0', 'C1', 'C2', 'C3', 'C4', 'C5' +] + +with open(json_template_file, 'r') as f: + data_json = json.load(f) + +grid = data_json['performance']['performance_map_cooling']['grid_variables'] +keys = list(grid.keys()) +values = [grid[k] for k in keys] +combinations = list(itertools.product(*values)) + +def _pick(df, t): + s = df[df['CurveType'] == t] + if s.empty: + raise RuntimeError(f"CurveType '{t}' not found in {csv_file}") + return s.iloc[0] + +cap_f_t_row = _pick(df_curves, 'cap-f-t') +cap_f_flow_row= _pick(df_curves, 'cap-f-ff') +eir_f_t_row = _pick(df_curves, 'eir-f-t') +eir_f_flow_row= _pick(df_curves, 'eir-f-ff') +plf_f_plr_row = _pick(df_curves, 'plf-f-plr') + +lookup = { + "gross_total_capacity": [], + "gross_sensible_capacity": [], + "gross_power": [], + "operation_state": [] +} + +for combo in combinations: + combo_dict = dict(zip(keys, combo)) + try: + Tdbi_K = combo_dict["indoor_coil_entering_dry_bulb_temperature"] + RH_frac = max(0.01, min(1.0, combo_dict["indoor_coil_entering_relative_humidity"])) + Tdbo_K = combo_dict["outdoor_coil_entering_dry_bulb_temperature"] + P_kPa = combo_dict["ambient_absolute_air_pressure"] + + Tdbo_C = Tdbo_K - 273.15 + WBi_C = compute_wetbulb(Tdbi_K, RH_frac, P_kPa) + + flow_rate = combo_dict["indoor_coil_air_mass_flow_rate"] # kg/s + flow_ratio = flow_rate / max(grid["indoor_coil_air_mass_flow_rate"]) + plr = 1.0 # fixed PLR for now + + # Curves use indoor wet-bulb and outdoor dry-bulb + gross_cap, _, power = calculate_performance( + cap_f_t_row, cap_f_flow_row, eir_f_t_row, eir_f_flow_row, plf_f_plr_row, + WBi_C, Tdbo_C, flow_ratio, plr, + nominal_capacity, nominal_eir + ) + + # --- Apply stage degradation, if present --- + comp_stage = combo_dict.get("compressor_sequence_number", 1) + if comp_stage > 1: + degradation_factor = 0.5 + gross_cap *= degradation_factor + power *= degradation_factor + + # --- Sensible capacity via ADP method --- + sens_cap, _, _ = estimate_sensible_capacity_coolpropline( + Q_total_W=gross_cap, + Tdbi_K=Tdbi_K, + RH_frac=RH_frac, + flow_rate_kg_s=flow_rate, + pressure_kPa=P_kPa, + SHR_rated=nominal_SHR + ) + + lookup["gross_total_capacity"].append(gross_cap) + lookup["gross_sensible_capacity"].append(sens_cap) + lookup["gross_power"].append(power) + lookup["operation_state"].append("NORMAL") + + except Exception as e: + print(f"Skipping point {combo_dict} due to error: {e}") + continue + +data_json['performance']['performance_map_cooling']['lookup_variables'] = lookup + +Path(output_json_file).parent.mkdir(parents=True, exist_ok=True) +with open(output_json_file, 'w') as f: + json.dump(data_json, f, indent=2) + +print(f"โœ… STD205 JSON updated and saved to: {output_json_file}") + + +# ------------------------- +# STEP 3: Validate JSON +# ------------------------- +base_dir = Path.cwd() +schema_file = base_dir / "RS0004.schema.json" +ashrae_file = base_dir / "ASHRAE205.schema.json" +json_file = base_dir / output_json_file + +with schema_file.open() as f: + schema = json.load(f) +with ashrae_file.open() as f: + ashrae_schema = json.load(f) + +ashrae_uri = ashrae_file.resolve().as_uri() +rs0004_uri = schema_file.resolve().as_uri() + +store = {ashrae_uri: ashrae_schema, rs0004_uri: schema} +resolver = RefResolver(base_uri=rs0004_uri, referrer=schema, store=store) + +with json_file.open() as f: + data_val = json.load(f) + +validator = Draft7Validator(schema, resolver=resolver) +errors = sorted(validator.iter_errors(data_val), key=lambda e: e.path) + +if not errors: + print(f"โœ… {json_file.name} is valid according to {schema_file.name}") +else: + print(f"โŒ {json_file.name} has {len(errors)} validation errors:") + for err in errors: + path = ".".join(str(x) for x in err.path) + print(f" - {path}: {err.message}") + +# ------------------------- +# STEP 4: Convert JSON to XLSX using tk205 +# ------------------------- +src_dir = "input" # directory with JSON files +out_dir = "xlsx" # output directory + +tk205.translate_directory(src_dir, out_dir) +print(f"โœ… Converted JSON in {src_dir} โ†’ XLSX in {out_dir}") + +# ------------------------- +# STEP 5: Plot validation figures (curves vs JSON table) +# ------------------------- +figs_dir = Path("figs") +figs_dir.mkdir(exist_ok=True, parents=True) + +# ---- 5.1: Load curves (bi-quad coefficients for eir-f-t and cap-f-t) ---- +dfc = df_curves +dfc.columns = [ + "CurveName","CurveType","Unused","CurveUse", + "X1Min","X1Max","X2Min","X2Max", + "C0","C1","C2","C3","C4","C5" +] + +def _row_to_coeffs(df, curve_type): + row = df[df["CurveType"] == curve_type] + if row.empty: + raise RuntimeError(f"CurveType '{curve_type}' not found in {csv_file}") + return row.iloc[0][["C0","C1","C2","C3","C4","C5"]].astype(float).to_numpy() + +eir_coeffs = _row_to_coeffs(dfc, "eir-f-t") +cap_coeffs = _row_to_coeffs(dfc, "cap-f-t") + +def bi_quad(x, y, c): + return c[0] + c[1]*x + c[2]*x**2 + c[3]*y + c[4]*y**2 + c[5]*x*y + +def bi_quad_norm(x, y, c, x_ref, y_ref): + return bi_quad(x, y, c) / bi_quad(x_ref, y_ref, c) + +# ---- 5.2: Build a performance table from the JSON we just wrote ---- +with open(output_json_file, "r") as f: + std205 = json.load(f) + +grid = std205["performance"]["performance_map_cooling"]["grid_variables"] +lookup = std205["performance"]["performance_map_cooling"]["lookup_variables"] + +# Recreate combinations deterministically from the JSON grid +grid_keys = list(grid.keys()) +grid_vals = [grid[k] for k in grid_keys] +grid_combos = list(itertools.product(*grid_vals)) + +# Sanity check: combos length must match lookup arrays +n_pts = len(grid_combos) +for k in ["gross_total_capacity", "gross_power"]: + if len(lookup[k]) != n_pts: + raise RuntimeError(f"Lookup '{k}' length {len(lookup[k])} != grid size {n_pts}") + +# Build a compact DataFrame for plotting +perf_rows = [] +for i, combo in enumerate(grid_combos): + cd = dict(zip(grid_keys, combo)) + perf_rows.append({ + "indoor_db_C": cd["indoor_coil_entering_dry_bulb_temperature"] - 273.15, + "outdoor_db_C": cd["outdoor_coil_entering_dry_bulb_temperature"] - 273.15, + "capacity_W": float(lookup["gross_total_capacity"][i]), + "power_W": float(lookup["gross_power"][i]), + }) +perf = pd.DataFrame(perf_rows).dropna(subset=["capacity_W","power_W"]) + +# ---- 5.3: Choose a common reference point and compute modifiers ---- +target_in_C, target_out_C = 20.0, 30.0 +# Find nearest actual table point to the desired reference +i_ref = ((perf["indoor_db_C"] - target_in_C).abs() + + (perf["outdoor_db_C"] - target_out_C).abs()).idxmin() +cap_ref = perf.loc[i_ref, "capacity_W"] +eir_ref = perf.loc[i_ref, "power_W"] / max(perf.loc[i_ref, "capacity_W"], 1e-9) + +perf["cap_mod"] = perf["capacity_W"] / max(cap_ref, 1e-9) +perf["eir_mod"] = (perf["power_W"] / perf["capacity_W"]) / max(eir_ref, 1e-9) + +# ---- 5.4: Produce 1D comparisons vs outdoor temp for each indoor temp ---- +outdoor_span = np.linspace(perf["outdoor_db_C"].min(), perf["outdoor_db_C"].max(), 200) + +def curve_line(indoor_C, outdoor_arr, coeffs): + return np.array([bi_quad_norm(indoor_C, o, coeffs, target_in_C, target_out_C) for o in outdoor_arr]) + +# EIR modifier plot +plt.figure(figsize=(7, 5)) +for indoor_C in sorted(perf["indoor_db_C"].unique()): + subset = perf[perf["indoor_db_C"] == indoor_C] + eir_curve = curve_line(indoor_C, outdoor_span, eir_coeffs) + plt.plot(outdoor_span, eir_curve, label=f"Curve {indoor_C:.0f}ยฐC") + plt.scatter(subset["outdoor_db_C"], subset["eir_mod"], marker="x", label=f"Table {indoor_C:.0f}ยฐC") +plt.title("EIR modifier vs Outdoor Dry-Bulb") +plt.xlabel("Outdoor Dry-Bulb (ยฐC)") +plt.ylabel("EIR Modifier (norm. to ~20ยฐC/30ยฐC)") +plt.legend() +eir_fig_path = figs_dir / "eir_modifier_vs_outdoor.png" +plt.tight_layout() +plt.savefig(eir_fig_path, dpi=200) +plt.show() +print(f"๐Ÿ“ˆ Saved: {eir_fig_path}") + +# Capacity modifier plot +plt.figure(figsize=(7, 5)) +for indoor_C in sorted(perf["indoor_db_C"].unique()): + subset = perf[perf["indoor_db_C"] == indoor_C] + cap_curve = curve_line(indoor_C, outdoor_span, cap_coeffs) + plt.plot(outdoor_span, cap_curve, label=f"Curve {indoor_C:.0f}ยฐC") + plt.scatter(subset["outdoor_db_C"], subset["cap_mod"], marker="x", label=f"Table {indoor_C:.0f}ยฐC") +plt.title("Capacity modifier vs Outdoor Dry-Bulb") +plt.xlabel("Outdoor Dry-Bulb (ยฐC)") +plt.ylabel("Capacity Modifier (norm. to ~20ยฐC/30ยฐC)") +plt.legend() +cap_fig_path = figs_dir / "capacity_modifier_vs_outdoor.png" +plt.tight_layout() +plt.savefig(cap_fig_path, dpi=200) +plt.show() +print(f"๐Ÿ“ˆ Saved: {cap_fig_path}") \ No newline at end of file diff --git a/applications/ashrae205/xlsx/AC_Perf_901_2022_135_to_240_10.91EER_14.2IEER.xlsx b/applications/ashrae205/xlsx/AC_Perf_901_2022_135_to_240_10.91EER_14.2IEER.xlsx new file mode 100644 index 00000000..ea48ffd8 Binary files /dev/null and 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b/applications/ashrae205/xlsx/AC_Perf_901_2022_gt760_9.86EER_12.5IEER.xlsx differ diff --git a/copper/__init__.py b/copper/__init__.py index 76718138..f6f8ba60 100644 --- a/copper/__init__.py +++ b/copper/__init__.py @@ -1,8 +1,9 @@ +import logging +import sys from copper.chiller import * from copper.generator import * from copper.schema import * from copper.unitarydirectexpansion import * from copper.constants import LOGGING_FORMAT -import sys logging.basicConfig(format=LOGGING_FORMAT, stream=sys.stdout, level=logging.INFO)