Miah Md Saef Ullah, Sulaiman Junaida, Islam Md Imamul, Masuduzzaman Md, Lipu Molla Shahadat Hossain, Nugraha Ramdhan
Department of Computer Science, American International University-Bangladesh, Dhaka, Dhaka, Bangladesh.
Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia.
PeerJ Comput Sci. 2025 Apr 23;11:e2819. doi: 10.7717/peerj-cs.2819. eCollection 2025.
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 the United Nations (UN) Sustainable Development Goal (SDG) 7 (Affordable and Clean Energy). 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 (Industry, Innovation and Infrastructure). In this article, 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 (Climate Action) 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.
随着世界朝着符合联合国（UN）可持续发展目标（SDG）7（ affordable and Clean Energy）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越来越重要。然而，可再生能源的间歇性使得管理电网并确保稳定的电力供应具有挑战性，而这对于实现SDG 9（Industry, Innovation and Infrastructure）至关重要。在本文中，我们提出了一种用于预测智能电网中能源需求的深度学习模型，该模型可以通过提供准确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SDG 13（Climate Action），能够更有效地管理可再生能源资源。我们使用非常适合时间序列数据的长短期记忆网络来捕捉能源需求数据中的复杂模式和依赖性。我们使用来自不同能源分配公司（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）的四个历史短期能源需求数据数据集对所提出的方法进行了评估。将所提出的模型与其他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，这表明其有潜力提高电网的稳定性和效率，并有助于实现SDG 7、9和13。所提出的模型还有潜力有效管理可再生能源的整合。
