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Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique

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Abstract

This paper proposes the day-ahead electricity price forecasting using the artificial neural networks (ANN) and weighted least square (WLS) technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices (LMP) in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach proposed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland (PJM) interconnection is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.

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Authors and Affiliations

  1. Department of Railroad and Electrical Engineering, Woosong University, Daejeon, 300718, Republic of Korea

    S. Surender Reddy

  2. Department of Railroad and Civil Engineering, Woosong University, Daejeon, 300718, Republic of Korea

    Chan-Mook Jung

  3. Korea Rail Network Authority, Daejeon, 300718, Republic of Korea

    Ko Jun Seog

Authors
  1. S. Surender Reddy
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  2. Chan-Mook Jung
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  3. Ko Jun Seog
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Correspondence to Chan-Mook Jung.

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Reddy, S.S., Jung, CM. & Seog, K.J. Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique. Front. Energy 10, 105–113 (2016). https://doi.org/10.1007/s11708-016-0393-y

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  • DOI: https://doi.org/10.1007/s11708-016-0393-y

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