Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Abstract

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company

“…Models of [ 4 ] also contain some deep hybrid methods, which motivated us to use the deep hybrid methods. Although current research has had promising results in favor of machine learning methods, Lasso regression applications [ 6 , 7 ], ensemble predictions [ 8 – 10 ], and hybrid works [ 11 – 14 ] also have successful results. One important example is the work of Chaabane [ 11 ], which combines SARIMA with Auto-Regressive Fractionally Integrated Moving Average (ARFIMA).…”