Abstract
Accurate electricity price prediction is key to the orderly operation of the electricity market. However, the uncertain, stochastic and fluctuant characteristics of electricity pricees make prediction difficult. With the aim of solving this issue, this investigation proposed a multi-stage intelligent model integrating the Beveridge–Nelson decomposition (B-N-D) model, the least square support vector machine (LSSVM), and a nature-inspired optimization model named the whale optimization algorithm (WOA). Firstly, the B-N-D model was utilized to decompose the hourly electricity price time series into determinacy component, periodic trend, and stochastic item. Secondly, the WOA–LSSVM model was proposed to forecast the future hourly data of three components respectively, of which the optimal parameters of LSSVM were determined by using WOA. Finally, the future hourly electricity price data were computed by multiplying the forecasted data of those terms. To verify the validity of the proposed electricity price prediction model in this paper, five comparison approaches based on the B-N-D approach were selected, which are auto-regressive integrated moving average (ARIMA), single LSSVM, LSSVM optimized by the fruit-fly optimization algorithm (FOA), LSSVM optimized by particle swarm optimization (PSO) models, and WOA–LSSVM without B-N-D. By comparatively analyzing the error criteria values of the above models through testing on the objective data of the Pennsylvania–New Jersey–Maryland (PJM) electricity market collected from 11 December 2017 to 18 December 2017, from 15 January 2018 to 22 January 2018, and from 1 February 2018 to 25 February 2018, we conclude that the constructed intelligent model in this paper can greatly enhance the prediction precision of electricity prices.
Highlights
As the core of electricity market operations, accurate electricity price forecasting can help market participators to formulate reasonable competitive strategies and achieve risk minimization as well as benefit maximization [1]
After obtaining the natural forms of the deterministic trend, periodic term, and stochastic impact effect of hourly electricity price, the least square support vector machine (LSSVM) optimized by whale optimization algorithm (WOA) is utilized to respectively predict these three items, and the electricity price data can be predicted via multiplying the forecasted data of those terms
With the aim of comparing the prediction performances, five comparison prediction approaches are selected including auto-regressive integrated moving average (ARIMA), single LSSVM, LSSVM optimized by fruit-fly optimization algorithm (FOA-LSSVM), and LSSVM optimized by particle swarm optimization (PSO-LSSVM) models based on Beveridge–Nelson decomposition (B-N-D) approach, as well as WOA-LSSVM based on the initial electricity price data without B-N-D, testing at three different periods the electricity price data collected from the Pennsylvania–New Jersey–Maryland (PJM) electricity markets
Summary
As the core of electricity market operations, accurate electricity price forecasting can help market participators to formulate reasonable competitive strategies and achieve risk minimization as well as benefit maximization [1]. For power-generation enterprises, according to accurate electricity price prediction decision makers need to assign an appropriate proportion of electricity to participate in day-ahead market transactions, real-time market transactions, auxiliary services market transactions, and bilateral contract transactions. They need to formulate corresponding bidding tactics to obtain maximum return and minimum risks [2]. Mandal et al [16] put forward an integrated intelligent method combining a data-screening method according to the WT approach, an optimization algorithm on the basis of the firefly model, and a soft calculating technique using a fuzzy art-map (FA) network to predict day-ahead power priced for the Ontario electricity market.
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