Integrating 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 model 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, enabling more efficient management of renewable energy resources. We use long short-term memory networks, well-suited for time series data, to capture complex patterns and dependencies in energy demand data. The proposed approach is evaluated using four historical short-term energy demand data datasets 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 compared with three other state-of-the-art forecasting algorithms: Facebook Prophet, Support Vector Regression, and Random Forest Regression. 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 has the potential to manage the integration of renewable energy sources effectively.

翻译：随着世界朝着符合可持续发展目标7（SDG 7）的可持续能源未来迈进，将可再生能源整合到电网中正变得日益重要。然而，可再生能源的间歇性特征给电网管理和确保稳定供电带来了挑战，而稳定供电对于实现可持续发展目标9（SDG 9）至关重要。本文提出了一种用于预测智能电网能源需求的深度学习模型，该模型可通过提供精准的能源需求预测来改善可再生能源的整合。我们的方法符合关于气候行动的可持续发展目标13（SDG 13），能够实现对可再生能源的更高效管理。我们采用长短期记忆网络（该网络非常适合处理时间序列数据）来捕捉能源需求数据中的复杂模式与依赖关系。所提出的方法使用来自不同能源配电公司的四个历史短期能源需求数据集进行评估，这些公司包括美国电力公司、联邦爱迪生公司、代顿电力与照明公司以及宾夕法尼亚-新泽西-马里兰互联电网。所提出的模型与另外三种先进的预测算法进行了比较：Facebook Prophet、支持向量回归和随机森林回归。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，这表明其具有增强电网稳定性和效率、并助力实现可持续发展目标7、9和13的潜力。该模型还具有有效管理可再生能源整合的潜力。