Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Abstract

Accurate system marginal price and load forecasts play a pivotal role in economic power dispatch, system reliability and planning. Price forecasting helps electricity buyers and sellers in an energy market to make effective decisions when preparing their bids and making bilateral contracts. Despite considerable research work in this domain, load and price forecasting still remains to be a complicated task. Various uncertain elements contribute to electricity price and demand volatility, such as changes in weather conditions, outages of large power plants, fuel cost, complex bidding strategies and transmission congestion in the power system. Thus, to deal with such difficulties, we propose a novel hybrid deep learning method based upon bidirectional long short-term memory (BiLSTM) and a multi-head self-attention mechanisms that can accurately forecast locational marginal price (LMP) and system load on a day-ahead basis. Additionally, ensemble empirical mode decomposition (EEMD), a data-driven algorithm, is used for the extraction of hidden features from the load and price time series. Besides that, an intuitive understanding of how the proposed model works under the hood is demonstrated using different interpretability use cases. The performance of the presented method is compared with existing well-known techniques applied for short-term electricity load and price forecast in a comprehensive manner. The proposed method produces considerably accurate results in comparison to existing benchmarks.INDEX TERMS Deep learning, energy markets, ensemble empirical mode decomposition, multi-head self-attention, short-term load and price forecasting, kalman smoothing, model interpretability.

Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

“…Following model fitting, in order to assess the forecasting performance of the proposed tool under new power system operating scenarios, tests have been carried out with test data (i.e., unseen data). To perform this assessment, the following regression performance metrics that allows multi-output and have been focused on these issues were included: R-squared (R 2 ) [20], mean square error (MSE) [32] mean absolute error (MAE) [2,20], and mean absolute percentage error (MAPE) [33], which are defined in Equations ( 8)-( 11):…”

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

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“…In recent years, attention was also widely used in the prediction field. Azam [24] proposed a new hybrid deep learning method based on bi-directional long short-term memory and a multi-head self-attention mechanism, which can accurately predict the marginal price of position and the system load one day ago. By analyzing the formation mechanism of NOx and the reaction mechanism of the SCR reactor, a sequence-to-sequence dynamic prediction model was proposed by Xie [25], which can fit multivariable coupling, nonlinear, and large delay systems.…”

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“…The method primarily based on Empirical Mode Decomposition (EMD) [15], can decompose the original load sequence. However, the decomposed components are susceptible to model mixing, which can impact the accuracy, Ensemble Empirical Mode Decomposition (EEMD) [16] adds Gaussian white noise prior to decomposition, which can effectively alleviate the mode mixing phenomenon of empirical mode decomposition mode decomposition. However, the method's robustness to measurement noise needs to be strengthened.…”