Key technologies for load forecasting in new power systems and their applications in diverse scenario

doi:10.3969/j.issn.2097-0706.2025.03.005

Abstract

Abstract:

To achieve the goal of "dual carbon"， the new power system is transitioning towards greening， intelligence， and diversity. Load forecasting is crucial for ensuring the safe， economic， and reliable operation of the new power system. While traditional statistical methods perform well in forecasting load data with clear patterns， the high proportion of renewable energy and the stochastic user load in new power systems pose significant challenges to these methods. Artificial intelligence technologies， particularly machine learning and deep learning， have become research hotspots due to their advantages in dealing with complex data and extracting patterns， effectively improving the accuracy and robustness of load forecasting. In this context， load forecasting methods based on mathematical and statistical principles are reviewed and their limitations are discussed in this study. The latest advancements in applications of AI techniques in load forecasting are summarized， and the characteristics of traditional machine learning， deep learning， and hybrid forecasting models are analysed. Technical challenges of load forecasting and key applications under these five scenarios are summarized and discussed：regional system-level load forecasting， net load forecasting under high proportion of renewable energy scenarios，integrated energy system load forecasting in multi-type heterogeneous energy complementary scenarios， building load forecasting， and electric vehicle load forecasting. The future directions of load forecasting technologies are forecasted.

Key words: load forecasting, artificial intelligence, machine learning, deep learning, new power systems, electric vehicle load, integrated energy system load

CLC Number:

TK018：TM715

ZHANG Dongdong, LI Fangning, LIU Tianhao. Key technologies for load forecasting in new power systems and their applications in diverse scenario[J]. Integrated Intelligent Energy, 2025, 47(3): 47-61.

Figures/Tables 11

Table 1

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Table 2

Load forecasting challenges and application of key prediction techniques in each scenario

场景	预测难点	关键技术
区域系统级负荷预测	分布式能源接入、天气、重大节日等随机影响因素	基于机器学习或深度学习的组合预测方法、相关性分析
	大型区域系统级负荷长时间尺度的预测精度衰减	基于数理统计的预测方法、在线学习、机器学习
	小型配电区域级受局部用户行为短期波动性大	基于深度学习的组合预测方法
净负荷预测	净负荷本身数据缺失的情况	按成分预测方法，针对负荷和可再生能源应用不同的机器学习或深度学习方法建立预测模型
净负荷预测	净负荷数据内部中的可再生能源发电和负荷用电情况的不明晰	基于深度学习的整体预测方法、利用数据分解方法进行数据成分分解预测并重构的方法
综合能源系统负荷预测	不同类型能源负荷间的相互影响，多能源系统的负荷特性难协调	多任务学习框架
综合能源系统负荷预测	多种可再生能源的协同效应和互补性带来了复杂的非线性特性	基于深度学习的组合预测方法
建筑负荷预测	单一建筑用电随机性大、用电规律个性化、数据稀缺	通过深度学习分析非线性、通过联邦学习或迁移学习解决数据匮乏问题
建筑负荷预测	建筑用户集群内各建筑用电习惯差异大、特征分散	利用机器学习进行聚类、特征挖掘，再结合深度学习处理大规模数据的能力进行预测
电动汽车负荷预测	插充式电动汽车充电负荷需要同时考虑车辆的充电时间、位置分布以及充电功率的变化	基于深度学习的组合方法、图神经网络、线上线下联合学习
电动汽车负荷预测	换电式电动汽车的负荷预测需考虑换电站的电池库存管理及车辆的换电需求波动，受换电服务频率和车队运营模式的影响大	马尔科夫链、出行及电池状态的物理建模与机器学习的聚类方法相结合

Table 2

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分类	优点	缺点	常用方法	应用
监督学习	预测精度高、收敛速度快、评估方便	对标注数据依赖强、泛化能力受限	K-近邻算法	数据筛选^[25]
			随机森林	特征提取^[26]、负荷预测建模^[27]
			SVM	负荷预测建模^[17-18]
无监督学习	无需标注数据、可以发现数据内在结构、适用性广	解释性差、模型评估困难、不确定性高	K-means 聚类	数据聚类、相关性分析^[20]
			PCA	数据降维^[21,28]
			SOM	数据降维^[22]
半监督学习	标注数据需求少、有效利用无标签数据	模型复杂度高、评估不易、易受噪声干扰	自训练生成伪标签	将无标签数据转化为有标签数据，增加训练数据的多样性和数量^[24]
强化学习	适合动态环境、长期策略优化、自适应性强	探索成本高、计算资源需求大、模型不稳定性高	Q-learning	预测模型动态选择^[29]、用电行为决策学习^[30]