The integration of renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future in line with SDG 7. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity, which is crucial for achieving SDG 9. 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. Our approach aligns with SDG 13 on climate action as it enables more efficient management of renewable energy resources. 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 short term 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 three other state of the art forecasting algorithms namely, Facebook Prophet, Support Vector Regressor, and Random Forest Regressor. The experimental results show that the proposed REDf model can accurately predict energy demand with a mean absolute error of 1.4%, indicating its potential to enhance the stability and efficiency of the power grid and contribute to achieving SDGs 7, 9, and 13. The proposed model also have the potential to manage the integration of renewable energy sources in an effective manner.

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变得日益重要，这与SDG 7的目标一致。然而，可再生能源的间歇性特征给电网管理和稳定供电保障带来了挑战，而稳定供电是实现SDG 9的关键。本文提出一种基于深度学习的智能电网能源需求预测方法，通过提供准确的能源需求预测来改善可再生能源的整合。该方法有助于更高效地管理可再生能源资源，从而契合SDG 13关于气候行动的目标。我们采用长短期记忆网络（该网络特别适用于时间序列数据）来捕捉能源需求数据中的复杂模式和依赖关系。所提出的方法使用来自四个不同电力分销公司的历史短期能源需求数据集进行评估，包括American Electric Power、Commonwealth Edison、Dayton Power and Light以及Pennsylvania-New Jersey-Maryland Interconnection。同时，我们将该模型与三种最先进的预测算法（Facebook Prophet、支持向量回归和随机森林回归）进行比较。实验结果表明，所提出的REDf模型能够以1.4%的平均绝对误差准确预测能源需求，表明其在增强电网稳定性和效率方面具有潜力，有助于实现SDG 7、9和13。此外，该模型还有望有效管理可再生能源的整合。