Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

Venkataramana Veeramsetty, Dongari Rakesh Chandra, Francesco Grimaccia and Marco Mussetta
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Venkataramana Veeramsetty: Center for Artificial Intelligence and Deep Learning, Department of Electrical and Electronics Engineering, SR University, Warangal 506371, India
Dongari Rakesh Chandra: Department of Electrical and Electronics Engineering, Kakatiya Institute of Technology and Science (KITS), Warangal 506015, India
Francesco Grimaccia: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
Marco Mussetta: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy

Forecasting, 2022, vol. 4, issue 1, 1-16

Abstract: Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning. In this context, machine learning and data analytics models represent a valuable tool to cope with the intrinsic complexity and especially design future demand-side advanced services. The main novelty in this paper is that the combination of a Recurrent Neural Network (RNN) and Principal Component Analysis (PCA) techniques is proposed to improve the forecasting capability of the hourly load on an electric power substation. A historical dataset of measured loads related to a 33/11 kV MV substation is considered in India as a case study, in order to properly validate the designed method. Based on the presented numerical results, the proposed approach proved itself to accurately predict loads with a reduced dimensionality of input data, thus minimizing the overall computational effort.

Keywords: load forecasting; recurrent neural network; self adaptive Adam optimizer; Principal Component Analysis; Hourly Ahead Market (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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