Department of Mechanical, Energetic and Material Engineering, School of Industrial Engineering, University of Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain.
Department of Electrical Engineering, Electronics and Automation, School of Industrial Engineering, University of Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain.
Sensors (Basel). 2022 May 11;22(10):3664. doi: 10.3390/s22103664.
Short-term forecasting of electric energy consumption has become a critical issue for companies selling and buying electricity because of the fluctuating and rising trend of its price. Forecasting tools based on Artificial Intelligence have proved to provide accurate and reliable prediction, especially Neural Networks, which have been widely used and have become one of the preferred ones. In this work, two of them, Long Short-Term Memories and Gated Recurrent Units, have been used along with a preprocessing algorithm, the Empirical Mode Decomposition, to make up a hybrid model to predict the following 24 hourly consumptions (a whole day ahead) of a hospital. Two different datasets have been used to forecast them: a univariate one in which only consumptions are used and a multivariate one in which other three variables (reactive consumption, temperature, and humidity) have been also used. The results achieved show that the best performances were obtained with the multivariate dataset. In this scenario, the hybrid models (neural network with preprocessing) clearly outperformed the simple ones (only the neural network). Both neural models provided similar performances in all cases. The best results (Mean Absolute Percentage Error: 3.51% and Root Mean Square Error: 55.06) were obtained with the Long Short-Term Memory with preprocessing with the multivariate dataset.
短期电力消费预测已成为买卖双方的一个关键问题，因为电力价格具有波动性和上涨趋势。基于人工智能的预测工具已被证明可以提供准确可靠的预测，特别是神经网络，它已被广泛应用，并已成为首选之一。在这项工作中，使用了两种工具，长短期记忆和门控循环单元，以及一种预处理算法，经验模态分解，构成了一个混合模型，以预测医院未来 24 小时（全天）的电力消费。使用了两个不同的数据集进行预测：一个是仅使用消费数据的单变量数据集，另一个是还使用了其他三个变量（无功消耗、温度和湿度）的多变量数据集。所取得的结果表明，使用多变量数据集可以获得最佳性能。在这种情况下，混合模型（预处理后的神经网络）明显优于简单模型（仅神经网络）。在所有情况下，两种神经网络模型的性能都相似。使用预处理后的长短期记忆和多变量数据集，获得了最佳结果（平均绝对百分比误差：3.51%和均方根误差：55.06）。
