Faculty of Information Systems and Technologies, University of Donja Gorica, 81000 Podgorica, Montenegro.
Sensors (Basel). 2022 Jan 28;22(3):1051. doi: 10.3390/s22031051.
As artificial neural network architectures grow increasingly more efficient in time-series prediction tasks, their use for day-ahead electricity price and demand prediction, a task with very specific rules and highly volatile dataset values, grows more attractive. Without a standardized way to compare the efficiency of algorithms and methods for forecasting electricity metrics, it is hard to have a good sense of the strengths and weaknesses of each approach. In this paper, we create models in several neural network architectures for predicting the electricity price on the HUPX market and electricity load in Montenegro and compare them to multiple neural network models on the same basis (using the same dataset and metrics). The results show the promising efficiency of neural networks in general for the task of short-term prediction in the field, with methods combining fully connected layers and recurrent neural or temporal convolutional layers performing the best. The feature extraction power of convolutional layers shows very promising results and recommends the further exploration of temporal convolutional networks in the field.
随着人工神经网络架构在时间序列预测任务中的效率不断提高，它们在日前电力价格和需求预测方面的应用变得更加有吸引力，这是一项具有非常具体规则和高度不稳定数据集值的任务。由于没有一种标准化的方法来比较算法和预测电力指标的方法的效率，因此很难很好地了解每种方法的优缺点。在本文中，我们在几个神经网络架构中创建了用于预测 HUPX 市场电价和黑山电力负荷的模型，并在相同的基础上（使用相同的数据集和指标）将它们与多个神经网络模型进行了比较。结果表明，神经网络在该领域的短期预测任务中具有很高的效率，其中结合了全连接层和递归神经网络或时间卷积层的方法表现最佳。卷积层的特征提取能力显示出非常有前途的结果，并推荐在该领域进一步探索时间卷积网络。
