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Electricity price forecasting using neural networks and similar days
chapter
posted on 2023-05-24, 05:21 authored by Mandal, P, Srivastava, AK, Senjyu, T, Michael NegnevitskyMichael NegnevitskyThis 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.
History
Publication title
Advances in Electric Power and Energy Systems: Load and Price ForecastingEditors
ME El-HawaryPagination
215-249ISBN
9781118171349Department/School
School of EngineeringPublisher
John Wiley & Sons, IncPlace of publication
New Jersey, United StateExtent
8Rights statement
Copyright 2017 The Institute of Electrical and Electronics Engineers, Inc.Repository Status
- Restricted