Sustainable Planning of Electrical Power Generation
Table of Contents
Introduction
This project demonstrates how Predictive and Prescriptive models can be combined to support the complex process of planning the electrical power generation. One of the main challenges in the Energy & Electric Power Industry is obtaining effective production plans versus the uncertainty of demand and renewable generation. Here you can see how Machine Learning and Decision Optimization are leveraged to obtain effective business recommendations through multiple scenarios.
The predictive part of the project takes time series data in the form of electrical demand over the span of several years and trains and tests a model that is able to predict electrical demand into the future. This is implemented in two different ways to show how it can be done in both a drag-and-drop platform like SPSS, as well as writing code (python) in a Jupyter notebook. The first is an SPSS flow that trains a neural network model. The second is a python Jupyter notebook that trains an XGBoost Regressor model.
The Decision Optimization part of the project focuses on 3 different mathematical formulations of the Unit Commitment Problem, which are solved and compared by using IBM® Decision Optimization (DO) for Data Science Experience (DSX) Local.
The project is composed by the following assets:
- Notebooks:
- Predictive Model:
- SPSS Modeler Flow
- Ercot Demand
- DO Model:
- Datasets:
- A collection of CSV files representing multiple instances of the Unit Commitment problem, plus other files playing the integration between the project assets
Application Design
The Prediction assets of the project are organized into datasets, notebooks, SPSS Modeler Flows and models.
The dataset ERCOT_Hourly_Load_Data_2002-2016.csv is the historical data used for training and testing both the predictive notebook as well as the SPSS Flow.
The notebook dem-predict-electrical-demand trains on this data.
It then saves the trained model as XGBoost Electrical Demand.
Finally it uses the model to predict electrical demand for 2016, saving the results to ERCOT_Predictions_2016.csv.
The SPSS Flow was constructed to closely match what the predictive notebook does. It trains and tests the model on the same data. We used a Neural Net model node here instead of using XGBoost to show something different. it then saves the results to ERCOT_Predictions_2016_SPSS.csv
The Decision Optimization assets of the project give rise to a configurable framework to solve iteratively different formulations of the Unit Commitment
over multiple instances of the problem. The Optimization results arsing from the different scenarios are collected and
processed in order to visualize precise business recommendations, as well as summary KPIs that compare the quality of solutions
through an ex-post analysis.
Implementation
The implementation is based on SPSS Modeler Flows, Jupyter notebooks with Python 2.7 and 3.5, and DO Models for DSX. The main project assets are described in the following.
SPSS Modeler Flow ERCOT Demand
Uses Electrical Demand data from the Electric Reliability Council of Texas to predict future demand using Neural Network. Trains model using ERCOT_Hourly_Load_Data_2002-2016.csv for the years 2002-2015. Predicts hourly electrical load for 2016, writing to ERCOT_Predictions_2016_SPSS.csv.
Notebook dem-predict-electrical-demand
Uses Electrical Demand data from the Electric Reliability Council of Texas to predict future demand using XGBRegressor. Trains model using ERCOT_Hourly_Load_Data_2002-2016.csv for the years 2002-2015. Predicts hourly electrical load for 2016, writing to ERCOT_Predictions_2016.csv.
Notebook uc-formulations
This notebook implements 3 different Optimization approaches to the Unit Commitment problem. Each approach leads to a
mixed-integer linear program that is modeled and solved with
IBM® DO CPLEX® Modeling for Python:
docplex. The addressed formulations are described and discussed within the notebook.
The associated Python script is imported into the DO Model uc-models-comparison.
Notebook uc-opt-model-comparator
This notebook orchestrates the overall application. It allows to select one or more Optimization models implemented in
the notebook uc-formulations, then it allows to set up the desired problem instances to be solved. Hence, it executes the
configured Optimization jobs by managing the DO Model uc-models-comparison with the DSX Python APIs
dd_scenario. Finally, summary results produced by the
Optimization runs are collected and stored in a CSV file, which is consumed by the notebook display-comparison-kpis.
Notebook display-comparison-kpis
This notebook consumes the produced results to prepare the data and visualize summary charts. These charts compare
performance and solution quality of different Optimization models across multiple instances of the problem. Relevant
business KPIs are displayed by matplotlib charts in order to analyze the Optimization behaviour according to an ex-post
analysis.
Predictive Model XGBoost Electrical Demand
This predictive model is trained using XGBoost Regressor and is trained on electrical demand time series data.
It is able to predict future electrical demand data.
See the notebook dem-predict-electrical-demand for model creation details.
DO Model uc-models-comparison
This DO Model handles the Optimization approaches implemented in the notebook uc-formulations. It is managed by the
notebook uc-opt-model-comparator (it can be used interactively as well). In particular, a DO Scenario is created for each
selected formulation, then it is iteratively updated when a new instance of the problem has to be prepared and solved. This
DO Model contains also a dashboard that reports precise business recommendations for each scenario, and a summary chart
comparing cost KPIs across its multiple scenarios.