Skip to main content
SHARE
Publication

Gaussian Process Regression for Aggregate Baseline Load Forecasting...

by Kadir Amasyali, Mohammed M Olama
Publication Type
Conference Paper
Book Title
2021 Annual Modeling and Simulation Conference (ANNSIM)
Publication Date
Page Numbers
1 to 10
Conference Name
2021 Annual Modeling and Simulation Conference (ANNSIM)
Conference Location
Fairfax, Virginia, United States of America
Conference Date
-

Demand response (DR) is one of the most effective ways to maintain the reliability and improve the flexibility of power systems. Accurate forecasts of baseline loads are essential for DR programs. In the era of big data, machine learning-based approaches present a unique opportunity for baseline load forecasting. Thus, this paper presents a machine learning-based approach using a relatively less explored algorithm, Gaussian process regression (GPR), to forecast aggregate baseline loads. As such, a dataset was generated using a set of EnergyPlus simulations. Using the generated dataset, a GPR-based forecasting model was developed. In addition, support vector regression (SVR)-, artificial neural network (ANN)-, and averaging-based models were developed as baseline models for comparison. These models were compared in terms of accuracy, simplicity, and integrity. The prediction performance of the models showed that the GPR-based model is more accurate and reliable than the others. Such high performance shows the potential of the GPR in baseline load forecasting. GPR, therefore, can be used for DR applications.