Pallonetto, Fabiano and Jin, Changhong and Mangina, Eleni (2022) Forecast electricity demand in commercial building with machine learning models to enable demand response programs. Energy and AI, 7 (100121). ISSN 26665468

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Abstract

Electricity load forecasting is an important part of power system dispatching. Accurately forecasting electricity load have great impact on a number of departments in power systems. Compared to electricity load simulation (white-box model), electricity load forecasting (black-box model) does not require expertise in building construction. The development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load simulation. Recent developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear characteristics. Based on the real load dataset, this paper evaluates and compares the two mainstream short-term load forecasting techniques. Before the experiment, this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks (LSTMs) and Support Vector Machines (SVM) used in this paper. Secondly, based on the characteristics of the electricity load dataset, data pre-processing and feature selection takes place. This paper describes the results of a controlled experiment to study the importance of feature selection. The LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load forecasting. The predictive accuracy of these models are calculated based on the error between the actual and predicted loads, and the runtime of the model is recorded. The results show that the LSTMs model have a higher prediction accuracy when the load data is sufficient. However, the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.

Item Type:	Article
Keywords:	Deep neural network; Model assessment; Short-term load forecasting; Feature selection; Support Vector Machines; Artificial Neural Networks; Long Short-term Memory Networks; Demand response;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI Faculty of Social Sciences > School of Business
Item ID:	15602
Identification Number:	https://doi.org/10.1016/j.egyai.2021.100121
Depositing User:	Fabiano Pallonetto
Date Deposited:	01 Mar 2022 16:11
Journal or Publication Title:	Energy and AI
Publisher:	Elsevier
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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