Day-Ahead Electricity Price Forecasting in a Grid Environment

Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. Market participants rely on price forecasts to decide their bidding strategies, allocate assets, negotiate bilateral contracts, hedge risks, and plan facility investments. Market operators can also use electricity price forecasts to predict market power indexes for the purpose of monitoring participants' behaviors. Various forecasting techniques are applied to different time horizons for electricity price forecasting in locational marginal pricing (LMP) spot markets. Available correlated data also have to be selected to improve the short-term forecasting performance. In this paper, fuzzy inference system (FIS), least-squares estimation (LSE), and the combination of FIS and LSE are proposed. Based on extensive testing with various techniques, LSE provides the most accurate results, and FIS, which is also highly accurate, provides transparency and interpretability