Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Application of a New Hybrid Method for Day-Ahead Energy Price Forecasting in Iranian Electricity Market

Abstract- In a typical competitive electricity market, a large number of short-term and long-term contracts are set on basis of energy price by an Independent System Operator (ISO). Under such circumstances, accurate electricity price forecasting can play a significant role in improving the more reasonable bidding strategies adopted by the electricity market participants. So, they cannot only r...

Forecasting day ahead electricity price using ARMA methods

Day-ahead Price Forecasting of Electricity Markets by a New Hybrid Forecast Method

Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. Accordingly, in this paper a new strategy is proposed for electricity price forecast. The forecast strategy includes Wavelet Transform (WT...

Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network

Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs) resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. The LMP reveals important information for market participants in developing their bidding strategies. Moreover, LMP is also a vital indicator fo...

Forecasting day-ahead electricity prices in Europe: the importance of considering market integration

Motivated by the increasing integration among electricity markets, in this paper we propose three different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of t...