Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

Sergey Voronin,Jarmo Partanen

doi:10.3390/en6115897

Abstract

A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and price spike predictions. The normal price module is a mixture of wavelet transform, linear AutoRegressive Integrated Moving Average (ARIMA) and nonlinear neural network models. The probability of a price spike occurrence is produced by a compound classifier in which three single classification techniques are used jointly to make a decision. Combined with the spike value prediction technique, the output from the price spike module aims to provide a comprehensive price spike forecast. The overall electricity price forecast is formed as combined normal price and price spike forecasts. The forecast accuracy of the proposed method is evaluated with real data from the Finnish Nord Pool Spot day-ahead energy market. The proposed method provides significant improvement in both normal price and price spike prediction accuracy compared with some of the most popular forecast techniques applied for case studies of energy markets.

Highlights

Electricity price forecasting has become an important area of research in the aftermath of the worldwide deregulation of the power industry
Based on the needs of the energy market, a variety of approaches for electricity price forecasting have been proposed in the last decades, among them, models based on simulation of power system equipment and related cost information [2], game-theory based models which focus on the impact of bidder strategic behavior on electricity prices [3], models based on stochastic modeling of finance [4], regression models [5] and artificial intelligence models [6,7,8,9]
While most existing approaches to forecasting electricity prices are reasonably effective for normal prices, they cannot deal with the price spikes accurately

Summary

Introduction

Electricity price forecasting has become an important area of research in the aftermath of the worldwide deregulation of the power industry. Electricity price series can exhibit variable means, major volatility and significant spikes [1]. Price spikes were truncated before application of the forecasting model to reduce the influence of such observations on the estimation of the model parameters [12,13]. Electricity price spikes, are significant for energy market participants to stay competitive in a competitive market

Methods

Results

Conclusion