Department of Computer Engineering, Akdeniz University, Antalya 07070, Turkey.
Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar.
Sensors (Basel). 2021 Oct 27;21(21):7115. doi: 10.3390/s21217115.
Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques.
为终端用户提供稳定、低价且安全的能源供应是一项具有挑战性的任务。能源服务提供商受到天气、波动性和特殊事件等多种事件的影响。因此，预测这些事件并为采取预防措施留出时间窗口对于服务提供商至关重要。电力负荷预测可以建模为时间序列预测问题。一种解决方案是在时间序列中捕获此类时间网络的空间相关性、时空关系和时间依赖性。以前，已经使用了不同的机器学习方法来进行时间序列预测任务；但是，仍然需要新的研究来提高短期负荷预测模型的性能。在本文中，我们提出了一种新颖的深度学习模型，该模型使用基于双阶段注意力的递归神经网络来预测电力负荷消耗，其中注意力机制用于编码器和解码器阶段。编码器注意力层从输入向量中识别重要特征，而解码器注意力层用于克服使用固定上下文向量的限制，并提供更长的记忆容量。所提出的模型在平均绝对误差 (MAE) 和均方根误差 (RMSE) 分数方面提高了短期负荷预测 (STLF) 的性能。为了评估所提出模型的预测性能，在实验中使用了 UCI 家庭电力消耗 (HEPC) 数据集。实验结果表明，所提出的方法优于以前采用的技术。
