Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework
| Autor: | Muhammad Furqan Azam, Muhammad Shahzad Younis |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Kalman smoothing
Mathematical optimization General Computer Science short-term load and price forecasting Computer science 020209 energy Reliability (computer networking) 02 engineering and technology Electric power system 0202 electrical engineering electronic engineering information engineering General Materials Science Energy market Time series multi-head self-attention ensemble empirical mode decomposition Interpretability business.industry General Engineering Deep learning Bidding TK1-9971 energy markets 020201 artificial intelligence & image processing Electrical engineering. Electronics. Nuclear engineering Electricity Volatility (finance) business |
| Zdroj: | IEEE Access, Vol 9, Pp 85918-85932 (2021) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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