The integration of renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity. In this paper, we propose a deep learning-based approach for predicting energy demand in a smart power grid, which can improve the integration of renewable energy sources by providing accurate predictions of energy demand. We use long short-term memory networks, which are well-suited for time series data, to capture complex patterns and dependencies in energy demand data. The proposed approach is evaluated using four datasets of historical energy demand data from different energy distribution companies including American Electric Power, Commonwealth Edison, Dayton Power and Light, and Pennsylvania-New Jersey-Maryland Interconnection. The proposed model is also compared with two other state of the art forecasting algorithms namely, Facebook Prophet and Support Vector Regressor. The experimental results show that the proposed REDf model can accurately predict energy demand with a mean absolute error of 1.4%. This approach has the potential to improve the efficiency and stability of the power grid by allowing for better management of the integration of renewable energy sources.

翻译：随着全球迈向更可持续的能源未来，将可再生能源整合到电网中正变得愈发重要。然而，可再生能源的间歇性特征给电网管理和电力稳定供应带来了挑战。本文提出了一种基于深度学习的方法，用于预测智能电网中的能源需求，该方法通过提供准确的能源需求预测，能够改善可再生能源的整合效果。我们采用适用于时间序列数据的长短期记忆网络，以捕捉能源需求数据中的复杂模式和依赖关系。所提方法使用来自多家能源分销公司（包括美国电力公司、联邦爱迪生公司、代顿电力和照明公司以及宾夕法尼亚-新泽西-马里兰互联电网）的四组历史能源需求数据集进行评估。此外，该模型还与两种最新预测算法（即Facebook Prophet和支持向量回归器）进行了对比。实验结果表明，所提的REDf模型能够以1.4%的平均绝对误差准确预测能源需求。该方法通过优化可再生能源整合管理，具有提升电网效率和稳定性的潜力。