Short Term Electricity Price Forecasting based on Optimal Spike Interval Prediction

Bahrampour, Najmeh

doi:10.48306/estm.2026.583453.1019

Short Term Electricity Price Forecasting based on Optimal Spike Interval Prediction

Document Type : Original Article

Author

Najmeh Bahrampour

Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran

10.48306/estm.2026.583453.1019

Abstract

Increasing competition among electricity market players (mainly generation companies [GenCos]) emphasizes the significance of well-maintained operational planning, risk management, and decision-making. Electricity price forecasting plays a critical role in this respect. However, forecasting electricity prices is a complex and challenging task because price signals are nonlinear, non-stationary, and time-varying. Price fluctuations in power markets are both periodic and random, with price spikes being examples of events that severely affect forecasting performance. Although many forecasting methods have been proposed, few forecasting methods focus specifically on price spike information. There are two major challenges in price spike forecasting: predicting when a spike will occur and how large it will be. A typical assumption is that neural network (NN) information about expected future data points is equally valuable, or even more valuable. Prediction is mainly treated using an online NN approach. This paper proposes a novel approach for forecasting both the occurrence and value of electricity price spikes. The developed method is based on an improved neural network-based algorithm that calculates the upper and lower bounds of the price signal. Compared to traditional techniques, the proposed approach predicts both the price spike class and intensity in a single step. The method was evaluated using real data from several electricity markets. The obtained results indicate the accuracy and efficiency of the proposed method. The proposed method achieves up to 98% spike occurrence prediction accuracy (PJM 2006, $150/MWh threshold) and 96.9% (PJM 2008), correctly identifying most price spikes in real market data.

Keywords

electricity price spike

forecasting

input data model

lower bound of price

upper bound of price

optimization algorithm

Subjects

Energy Conversion and Management

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