【Applied Energy 最新原创论文】屋顶光伏供给与农宅负荷的不匹配分析：基于混合深度学习的可解释性负荷模式挖掘

•提出了一个用于日内短时光伏-负荷不匹配分析的框架;

•使用变分自编码器模型对高分辨率负荷数据进行降维;

•采用决策树算法对农村典型负荷模式进行季节性分类与可解释性补充;

•探究了光伏容量和数据时间分辨率对不匹配的影响;

Establishing a new type of electricity system based on rooftop photovoltaics (PV) can facilitate the energy transition in rural China. Research on the mismatch between the PV supply and rural household demand is vital to the widespread adoption of PV microgrid systems. Currently, typical load patterns (TLPs) in rural areas lack accurate characterization and mismatch assessment methods disregard PV curtailment. Therefore, this study proposes a hybrid deep learning-based analytical framework to quantify short-term mismatches between PV power generation and TLPs throughout the day and applies it to a real rural dataset. This study employs the variational autoencoder (VAE) model for dimensionality reduction and feature extraction of high-resolution load data and compares it with traditional methods. In addition, we employed the k-medoids method to uncover TLPs and utilized decision trees to enhance interpretability. The results show that (1) The VAE model exhibits superior dimensionality reduction and feature extraction capabilities on both public and measured datasets and compared to other models, it can reconstruct peak loads more effectively. (2) Three types of TLPs were identified within the rural dataset, with the outdoor average daily wet-bulb temperature being the major influencing factor. (3) Significant differences existed in the mismatch levels between the three types of TLPs and PV power generation. The Lorenz curves and Gini coefficients can effectively quantify the mismatch between PV power generation and TLPs. The proposed framework provides theoretical support for optimizing PV microgrid systems design in rural areas and developing demand-side response strategies.

Keywords

Typical load patterns;

Photovoltaic microgrid system；

Supply-demand mismatch

Deep learning；

Rural energy transition；

Fig. 1.Graphical abstract.

Fig. 2. Research framework.

Fig. 3. Comparison of reconstruction errors of the 3 methods on the 3 datasets. (a) Ruicheng; (b) Apt_UMass; (c) House_UMass

Fig. 4.Typical load patterns (TLPs) by clustering from the Ruicheng dataset. (a) TLP 1: Exhibits a total daily energy consumption of 5.29 kWh, characterized by amorning peak in load demand; (b) TLP 2: Shows a total daily energy consumption of 4.15 kWh, also with a morning peak load; (c) TLP 3: Demonstrates a total daily energy consumption of 2.95 kWh, but lacks a morning peak load.

Fig. 5. Classification tree developed for seasonal segmentation of TLPs.

       本研究由中国清华大学建筑学院的研究人员完成。

通讯作者简介：

       杨旭东, 清华大学建筑学院副院长、教授、国家级人才计划人选者、国际期刊Building and Environment主编、国际能源署建筑与社区节能委员会执委和中国代表、美国供热制冷和空调工程师协会会士（ASHRAE Fellow）。长期从事城乡建筑节能与可再生能源技术、绿色低碳社区研究和工程实践，成果被中央电视台、Nature等专题报道。发表SCI论文200余篇，拥有发明专利70余项，应邀在国际学术会议上做大会特邀或主题报告120多次。曾获美国CDC青年研究科学家奖、ASHRAE研究新人奖和杰出贡献奖、中国节能协会节能减排科技进步一等奖等。入选爱思唯尔建设和建造领域中国高被引学者、斯坦福大学全球前2%顶尖科学家终身榜单。

第一作者简介：

       高钉，博士研究生，清华大学建筑学院，从事农村新型电力系统、需求侧响应等领域研究。已在Applied Energy、Sustainable Energy Technologies and Assessments等期刊发表一作论文2篇。

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