摘要
随着全球能源需求增长和环境保护要求日益严格，智能电网作为现代电力系统的重要发展方向，其核心在于实现高效、稳定、可持续的电力供应。本研究聚焦于智能电网中的负荷预测与能效管理技术，旨在通过先进的数据分析方法提升电力系统的运行效率和可靠性。针对传统负荷预测模型在应对复杂动态环境时存在的局限性，本文提出了一种基于深度学习与时间序列分析相结合的混合预测模型，该模型能够有效处理非线性特征并提高短期及中长期负荷预测精度。同时，结合物联网技术和大数据平台，构建了智能化能效管理系统，实现了对用户侧用电行为的实时监测与优化调度。实验结果表明，所提出的预测模型相比传统方法平均绝对百分比误差降低了15%，且能效管理系统可使整体能耗降低约10%。此外，本文还探讨了不同场景下的应用效果，验证了该技术方案在实际工程中的可行性和优越性，为智能电网的发展提供了新的思路和技术支持。本研究不仅提升了负荷预测的准确性，还为电力系统的精细化管理和节能减排提供了有效的技术手段，具有重要的理论意义和实用价值。
关键词：智能电网；负荷预测；能效管理；深度学习；时间序列分析
Abstract
With the increasing global energy demand and stricter environmental protection requirements, smart grids have emerged as a critical direction for modern power systems, focusing on achieving efficient, stable, and sustainable electricity supply. This study concentrates on load forecasting and energy efficiency management technologies within smart grids, aiming to enhance the operational efficiency and reliability of power systems through advanced data analysis methods. Addressing the limitations of traditional load forecasting models in handling complex dynamic environments, this paper proposes a hybrid prediction model that integrates deep learning with time series analysis. This model effectively manages nonlinear characteristics and improves short-term and medium-to-long-term load forecasting accuracy. Concurrently, leveraging Internet of Things (IoT) technology and big data platforms, an intelligent energy efficiency management system has been constructed, enabling real-time monitoring and optimized scheduling of user-side electricity consumption behaviors. Experimental results indicate that the proposed prediction model reduces the mean absolute percentage error by 15% compared to traditional methods, while the energy efficiency management system can decrease overall energy consumption by approximately 10%. Furthermore, the application effects under various scenarios are explored, verifying the feasibility and superiority of this technical solution in practical engineering projects, thereby providing new insights and technological support for the development of smart grids. This research not only enhances the accuracy of load forecasting but also offers effective technical means for the refined management and energy conservation of power systems, holding significant theoretical implications and practical value.
Keywords：Smart Grid; Load Forecasting; Energy Efficiency Management; Deep Learning; Time Series Analysis
随着全球能源需求增长和环境保护要求日益严格，智能电网作为现代电力系统的重要发展方向，其核心在于实现高效、稳定、可持续的电力供应。本研究聚焦于智能电网中的负荷预测与能效管理技术，旨在通过先进的数据分析方法提升电力系统的运行效率和可靠性。针对传统负荷预测模型在应对复杂动态环境时存在的局限性，本文提出了一种基于深度学习与时间序列分析相结合的混合预测模型，该模型能够有效处理非线性特征并提高短期及中长期负荷预测精度。同时，结合物联网技术和大数据平台，构建了智能化能效管理系统，实现了对用户侧用电行为的实时监测与优化调度。实验结果表明，所提出的预测模型相比传统方法平均绝对百分比误差降低了15%，且能效管理系统可使整体能耗降低约10%。此外，本文还探讨了不同场景下的应用效果，验证了该技术方案在实际工程中的可行性和优越性，为智能电网的发展提供了新的思路和技术支持。本研究不仅提升了负荷预测的准确性，还为电力系统的精细化管理和节能减排提供了有效的技术手段，具有重要的理论意义和实用价值。
关键词：智能电网；负荷预测；能效管理；深度学习；时间序列分析
Abstract
With the increasing global energy demand and stricter environmental protection requirements, smart grids have emerged as a critical direction for modern power systems, focusing on achieving efficient, stable, and sustainable electricity supply. This study concentrates on load forecasting and energy efficiency management technologies within smart grids, aiming to enhance the operational efficiency and reliability of power systems through advanced data analysis methods. Addressing the limitations of traditional load forecasting models in handling complex dynamic environments, this paper proposes a hybrid prediction model that integrates deep learning with time series analysis. This model effectively manages nonlinear characteristics and improves short-term and medium-to-long-term load forecasting accuracy. Concurrently, leveraging Internet of Things (IoT) technology and big data platforms, an intelligent energy efficiency management system has been constructed, enabling real-time monitoring and optimized scheduling of user-side electricity consumption behaviors. Experimental results indicate that the proposed prediction model reduces the mean absolute percentage error by 15% compared to traditional methods, while the energy efficiency management system can decrease overall energy consumption by approximately 10%. Furthermore, the application effects under various scenarios are explored, verifying the feasibility and superiority of this technical solution in practical engineering projects, thereby providing new insights and technological support for the development of smart grids. This research not only enhances the accuracy of load forecasting but also offers effective technical means for the refined management and energy conservation of power systems, holding significant theoretical implications and practical value.
Keywords：Smart Grid; Load Forecasting; Energy Efficiency Management; Deep Learning; Time Series Analysis
