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Electricity price forecasting using neural networks and similar days

Citation

Mandal, P and Srivastava, AK and Senjyu, T and Negnevitsky, M, Electricity price forecasting using neural networks and similar days, Advances in Electric Power and Energy Systems: Load and Price Forecasting, John Wiley & Sons, Inc, ME El-Hawary (ed), New Jersey, United State, pp. 215-249. ISBN 9781118171349 (2017) [Research Book Chapter]

Copyright Statement

Copyright 2017 The Institute of Electrical and Electronics Engineers, Inc.

DOI: doi:10.1002/9781119260295.ch6

Abstract

This chapter focuses on day‐ahead forecasts of electricity price in the PJM market using artificial neural network (ANN) model based on the similar days (SD) method. The PJM competitive market is a regional transmission organization (RTO) that plays a vital role in the US electric system. The chapter contributes to forecast electricity prices in the day‐ahead market. In addition to the integration of SD and ANN method, it also proposes a new technique to forecast hourly electricity prices in the PJM market using a recursive neural network (RNN), which is based on the SD method. The proposed RNN model is also applied to generate the next three‐day price forecasts. To evaluate the performance of the proposed neural networks, the mean absolute percentage error (MAPE), mean absolute error (MAE), and forecast mean square error (FMSE) are calculated.

Item Details

Item Type:Research Book Chapter
Keywords:neural networks, electricity price forecasting
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Control Systems, Robotics and Automation
Objective Division:Manufacturing
Objective Group:Machinery and Equipment
Objective Field:Machinery and Equipment not elsewhere classified
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:124739
Year Published:2017
Deposited By:Engineering
Deposited On:2018-03-06
Last Modified:2018-06-18
Downloads:0

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