Short-Term Electricity Price Forecasting Based on Similar Day-Based Neural Network

Chun-Yao Lee and Chang-En Wu
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Chun-Yao Lee: Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan
Chang-En Wu: Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan

Energies, 2020, vol. 13, issue 17, 1-15

Abstract: This paper presents four refined distance models to the application of forecasting short-term electricity price namely Euclidean norm, Manhattan distance, cosine coefficient, and Pearson correlation coefficient. The four refined models were constructed and used to select the days, which are like a reference day in electricity prices and loads, called similar days in this study. Using the similar days, the electricity prices of a forecast day were further obtained by similar day regression (SDR) and similar day based artificial neural network (SDANN). The simulation results of the case of the PJM (Pennsylvania, New Jersey and Maryland) interchange energy market indicate the superiority and availability of the selection 45 framework days and three similar days based on Pearson correlation coefficient model.

Keywords: similar-day method; linear regression; artificial neural network; electricity price (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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