Abstract Forecasting of energy-related variables is crucial for accurate planning and management of electrical power grids, aiming at improving overall efficiency and performance. In this paper, an artificial neural network (ANN)-based model is investigated for short-term forecasting of the hourly wind speed, solar radiation, and electrical power demand. Specifically, the non-linear autoregressive network with exogenous inputs (NARX) ANN is considered, compared to other models, and then selected to perform multi-step-ahead forecasting. Different time horizons have been considered in the range between 8 and 24 h ahead. The simulation analysis has put in evidence the main advantage of the proposed method, i.e., its capability to reconcile good forecasting performance in the short-term time horizon with a very simple network structure, which is potentially implementable on a low-cost processing platform.

中文翻译：

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基于人工神经网络的电网管理能源相关时间序列预测模型

摘要 能源相关变量的预测对于电网的准确规划和管理至关重要，旨在提高整体效率和性能。在本文中，研究了一种基于人工神经网络 (ANN) 的模型，用于对每小时风速、太阳辐射和电力需求的短期预测。具体来说，与其他模型相比，考虑了具有外生输入 (NARX) ANN 的非线性自回归网络，然后选择执行多步提前预测。在未来 8 到 24 小时的范围内考虑了不同的时间范围。模拟分析证明了所提出方法的主要优点，即它能够在短期时间范围内与非常简单的网络结构协调良好的预测性能，