Merge pull request watertap-org#96 from zacharybinger/pv_surrogates

Adding PV Surrogate

src/watertap_contrib/reflo/solar_models/surrogate/pv/pv_surrogate.json

@@ -0,0 +1 @@
+{"model_encoding": {"annual_energy": {"attr": {"x_data_columns": ["design_size"], "x_data": [[0.707070707070707], [0.030303030303030304], [0.3131313131313131], [0.1919191919191919], [0.3535353535353535], [0.9191919191919191], [0.7474747474747474], [0.10101010101010101], [0.7676767676767676], [0.1414141414141414], [0.797979797979798], [0.2525252525252525], [0.7171717171717171], [0.6666666666666666], [0.7575757575757576], [0.06060606060606061], [0.3838383838383838], [0.3636363636363636], [0.9292929292929293], [0.40404040404040403], [0.4242424242424242], [0.1313131313131313], [0.050505050505050504], [0.5656565656565656], [0.8282828282828283], [0.0], [0.0101010101010101], [0.5252525252525252], [0.1616161616161616], [0.0808080808080808], [0.0707070707070707], [1.0], [0.24242424242424243], [0.7777777777777778], [0.9898989898989898], [0.9797979797979798], [0.8989898989898989], [0.8383838383838385], [0.5454545454545454], [0.4949494949494949], [0.7272727272727272], [0.2121212121212121], 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{"design_size": [1, 200000]}, "surrogate_type": "rbf"}

src/watertap_contrib/reflo/solar_models/surrogate/pv/pv_surrogate.py

@@ -0,0 +1,192 @@
+###############################################################################
+# WaterTAP Copyright (c) 2021, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National
+# Laboratory, National Renewable Energy Laboratory, and National Energy
+# Technology Laboratory (subject to receipt of any required approvals from
+# the U.S. Dept. of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#
+###############################################################################
+
+import os
+import sys
+import re
+import time
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from io import StringIO
+import matplotlib.pyplot as plt
+
+from pyomo.environ import ConcreteModel, Var, Constraint, units as pyunits, value, Param
+
+from idaes.core import FlowsheetBlock
+from idaes.core import declare_process_block_class
+from idaes.core.surrogate.surrogate_block import SurrogateBlock
+from idaes.core.surrogate.pysmo_surrogate import PysmoRBFTrainer, PysmoSurrogate
+from idaes.core.surrogate.sampling.data_utils import split_training_validation
+
+from watertap_contrib.reflo.core import SolarEnergyBaseData
+
+__author__ = "Zachary Binger, Matthew Boyd, Kurban Sitterley"
+
+
+@declare_process_block_class("PVSurrogate")
+class PVSurrogateData(SolarEnergyBaseData):
+    """
+    Surrogate model for PV.
+    """
+
+    CONFIG = SolarEnergyBaseData.CONFIG()
+
+    def build(self):
+        super().build()
+
+        self._tech_type = "PV"
+        self.surrogate_file = os.path.join(
+            os.path.dirname(__file__), "pv_surrogate.json"
+        )
+
+        self.design_size = Var(
+            initialize=1000,
+            bounds=[1, 200000],
+            units=pyunits.kW,
+            doc="PV design size in kW",
+        )
+
+        self.annual_energy = Var(
+            initialize=1,
+            units=pyunits.kWh,
+            doc="Annual energy produced by the plant in kWh",
+        )
+
+        self.land_req = Var(
+            initialize=7e7,
+            units=pyunits.acre,
+            doc="Land area required by the plant in acres",
+        )
+
+        self.surrogate_inputs = [self.design_size]
+        self.surrogate_outputs = [self.annual_energy, self.land_req]
+
+        self.input_labels = ["design_size"]
+        self.output_labels = ["annual_energy", "land_req"]
+
+        self.electricity_constraint = Constraint(
+            expr=self.annual_energy
+            == -1
+            * self.electricity
+            * pyunits.convert(1 * pyunits.year, to_units=pyunits.hour)
+        )
+
+    def load_surrogate(self):
+        print("Loading surrogate file...")
+        self.surrogate_file = os.path.join(
+            os.path.dirname(__file__), "pv_surrogate.json"
+        )
+
+        if os.path.exists(self.surrogate_file):
+            stream = StringIO()
+            oldstdout = sys.stdout
+            sys.stdout = stream
+
+            self.surrogate_blk = SurrogateBlock(concrete=True)
+            self.surrogate = PysmoSurrogate.load_from_file(self.surrogate_file)
+            self.surrogate_blk.build_model(
+                self.surrogate,
+                input_vars=self.surrogate_inputs,
+                output_vars=self.surrogate_outputs,
+            )
+
+            # Revert back to standard output
+            sys.stdout = oldstdout
+
+    def get_training_validation(self):
+        self.dataset_filename = os.path.join(
+            os.path.dirname(__file__), "data/dataset.pkl"
+        )
+        print("Loading Training Data...\n")
+        time_start = time.process_time()
+        pkl_data = pd.read_pickle(self.dataset_filename)
+        data = pkl_data.sample(n=int(len(pkl_data)))  # FIX default this to 100% of data
+        self.data_training, self.data_validation = split_training_validation(
+            data, self.training_fraction, seed=len(data)
+        )
+        time_stop = time.process_time()
+        print("Data Loading Time:", time_stop - time_start, "\n")
+
+    def create_surrogate(
+        self,
+        save=False,
+    ):
+        self.sample_fraction = 0.1  # fraction of the generated data to train with. More flexible than n_samples.
+        self.training_fraction = 0.8
+
+        self.get_training_validation()
+        time_start = time.process_time()
+        # Capture long output
+        stream = StringIO()
+        oldstdout = sys.stdout
+        sys.stdout = stream
+
+        # Create PySMO trainer object
+        trainer = PysmoRBFTrainer(
+            input_labels=self.input_labels,
+            output_labels=self.output_labels,
+            training_dataframe=self.data_training,
+        )
+
+        # Set PySMO options
+        trainer.config.basis_function = "gaussian"  # default = gaussian
+        trainer.config.solution_method = "algebraic"  # default = algebraic
+        trainer.config.regularization = True  # default = True
+
+        # Train surrogate
+        rbf_train = trainer.train_surrogate()
+
+        # Remove autogenerated 'solution.pickle' file
+        try:
+            os.remove("solution.pickle")
+        except FileNotFoundError:
+            pass
+        except Exception as e:
+            raise e
+        # Create callable surrogate object
+        xmin, xmax = [self.design_size.bounds[0]], [self.design_size.bounds[1]]
+        input_bounds = {
+            self.input_labels[i]: (xmin[i], xmax[i])
+            for i in range(len(self.input_labels))
+        }
+        rbf_surr = PysmoSurrogate(
+            rbf_train, self.input_labels, self.output_labels, input_bounds
+        )
+
+        # Save model to JSON
+        if (self.surrogate_file is not None) and (save is True):
+            print(f"Writing surrogate model to {self.surrogate_file}")
+            model = rbf_surr.save_to_file(self.surrogate_file, overwrite=True)
+
+        # Revert back to standard output
+        sys.stdout = oldstdout
+
+        time_stop = time.process_time()
+        print("Model Training Time:", time_stop - time_start, "\n")
+
+        return rbf_surr
+
+
+if __name__ == "__main__":
+    m = ConcreteModel()
+    m.fs = FlowsheetBlock(dynamic=False)
+    m.fs.pv = PVSurrogate()
+    m.fs.pv.create_surrogate(save=False)
+
+    m.fs.pv.load_surrogate()
+
+    results = m.fs.pv.surrogate.evaluate_surrogate(
+        m.fs.pv.data_validation[m.fs.pv.input_labels]
+    )
+    print(results)