Daily Peak Load Forecasting by Artificial Neural Network using Differential Evolutionary Particle Swarm Optimization Considering Outliers. Issue 4 (2019)

• Record Type: Journal Article
• Authors: Sakurai, Daiji; Fukuyama, Yoshikazu; Iizaka, Tatsuya; Matsui, Tetsuro
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 4(2019)
• Page Start: 389
• Page End: 394
• Publication Date: 2019
• Subjects: Load forecasting -- artificial neural networks -- evolutionary computation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.08.241
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper proposes an Artificial Neural Network (ANN) based daily peak load forecasting method by differential evolutionary particle swarm optimization (DEEPSO) considering outliers. When outliers exist in the training data, forecasting accuracy of daily peak load forecasting can be affected by the outliers. Therefore, engineers have removed the outliers from training data so far and it is a heavy burden for engineers. Utilization of evolutionary computation has a possibility to solve this problem. Moreover, forecasting accuracy may be improved using evolutionary computation techniques instead of the conventional stochastic gradient descent (SGD) with outliers. The proposed weights tuning method by DEEPSO is compared with the conventional weights tuning methods by SGD and PSO for verification of the efficacy of the proposed method.