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.

中文翻译：

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使用可解释的多头自注意力和基于 EEMD 的框架进行多视角电力负荷和价格预测

准确的系统边际电价和负荷预测在经济电力调度、系统可靠性和规划中起着举足轻重的作用。价格预测可帮助能源市场中的电力买家和卖家在准备投标和签订双边合同时做出有效决策。尽管在该领域开展了大量研究工作，但负载和价格预测仍然是一项复杂的任务。各种不确定因素会导致电价和需求波动，例如天气条件的变化、大型发电厂的停电、燃料成本、复杂的投标策略和电力系统的输电拥堵。因此，为了应对这些困难，我们提出了一种基于双向长短期记忆 (BiLSTM) 和多头自注意力机制的新型混合深度学习方法，可以准确地预测位置边​​际价格 (LMP) 和一天前的系统负载。此外，集成经验模式分解 (EEMD)，一种数据驱动算法，用于从负载和价格时间序列中提取隐藏特征。除此之外，还使用不同的可解释性用例展示了对所提议模型在幕后如何工作的直观理解。将所提出方法的性能与应用于短期电力负荷和价格预测的现有众所周知的技术进行综合比较。与现有基准相比，所提出的方法产生了相当准确的结果。