HIRA Model for Short-Term Electricity Price Forecasting

Abstract

In competitive power markets, electric utilities, power producers, and traders are exposed to increased risks caused by electricity price volatility. The growing reliance on renewable sources and their dependence on weather, nuclear uncertainty, market coupling, and global financial instability are contributing to the importance of accurate electricity price forecasting. Since power markets are not all equally developed, different price forecasting methods have been introduced for individual markets. The aim of this research is to introduce a short-term electricity price forecasting method that addresses the problems of price volatility, a varying number of input parameters, varying data availability, and a large number of parameters and input data. Furthermore, the proposed model can be used on any market as it targets the characteristics and specifics of each market. The proposed Hybrid Iterative Reactive Adaptive (HIRA) method consists of two phases. In analysis phase, fundamental parameters which affect the electricity price are identified depending on market development. Obtained parameters are used as data inputs for price forecasting using a hybrid method. The HIRA model combines a statistical approach for large data set analysis and a similar day method with neural network tools. Similar days are examined using a statistical method which combines correlation significance, price volatility, and forecasting accuracy of the historical data. Data are collected based on their availability and electricity prices are forecasted in several iterations. All relevant data for price forecasting are collected, categorized, and arranged using simple indicators which makes the HIRA model adaptive and reactive to new market circumstances. The proposed model is validated using the Hungarian Power Exchange (HUPX) electricity price data records. The results show that with HIRA model forecasting, the error is stable and does not depend on price volatility. The HIRA method has proven to be applicable for forecasting electricity prices in real-time market conditions and enables effective hedging of price risk in the production or market portfolio.

Forecasting One-Day-Ahead Electricity Prices for Italian Electricity Market Using Parametric and Nonparametric Approaches

“…Medium-term price forecasts (MTPF) range from a week to a few months and is generally required for generation expansion planning, maintenance scheduling, bilateral contacting, fuel contacting, developing investment and hedging strategies [8]. On the other hand, long-term price forecast (LTPF) generally covers time from several months to a few years and is generally used for planning and investment profitability analysis, such as making decisions for future investments in power plants, inducing sites and fuel sources [9]. As in many electricity markets daily prices are determined the day before the physical delivery by means of hourly concurrent auction, STPF has received higher attention in the literature.…”

Evolving deep neural networks for Time Series Forecasting

“…A limitation of the method is that neural network architecture is designed through heuristic methods. [14] used a Hybrid neural model (HIRA model) for Short-Term Electricity price forecasting, which was also applied and tested in the scope of two eletricity markets: German and Hungarian. In Hungary, thermal plants have 90% of installed capacity and represent a stable source of electricity, but generally involving a large installed capacity per unit.…”

“…In addition, we compared the average percentage error obtained by our system with the HIRA method [14], which was also applied and tested on two eletricity markets: German and Hungarian. It can be concluded from the results presented in section 2.3.2 that the LSTM Network can predict the value of the PLD with an error of up to 1.44%, which is lower than the value generated from multilayer perceptron networks and HIRA model [14].…”

Power Exchange Prices: Comparison of Volatility in European Markets

“…Reforms of liberalizing power industry started in Norway, United Kingdom, United States, and Chile in the late 1980s [3]. Technology advancement in the generation and in power networks, have enabled a separation of what had previously been a natural monopoly [4]. Borenstein and Bushnell in 2015 [5] as well as Newbery in 2004 [6] concluded that restructuring of electricity sector did not fulfill all expectations as costs overweighed the benefits.…”