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-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, 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 also 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 in an effective manner.

翻译：随着世界朝着符合可持续发展目标（SDG）7的更可持续能源未来迈进，将可再生能源整合到电网中变得日益重要。然而，可再生能源的间歇性特点使得电网管理和确保电力稳定供应（这对实现SDG 9至关重要）面临挑战。本文提出了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，可改善可再生能源的整合。我们的方法符合关于气候行动的SDG 13，能够更高效地管理可再生能源资源。我们采用适合时间序列数据的长短期记忆网络，捕捉能源需求数据中的复杂模式和依赖关系。利用来自四家不同能源分销公司（包括美国电力公司、联邦爱迪生公司、戴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）的历史短期能源需求数据集对所提方法进行了评估。同时，将该模型与三种业界最先进的预测算法（Facebook Prophet、支持向量回归和随机森林回归）进行了比较。实验结果表明，所提REDf模型能够以1.4%的平均绝对误差准确预测能源需求，显示了其在增强电网稳定性和效率方面的潜力，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。