@Article{ee.2026.077169,
AUTHOR = {Yinfeng Ma, Kuan Zhang, Youxin Chen, Nian Liu, Zhi Xu, Min Ren},
TITLE = {Hybrid Data and Model-Driven Multi-Energy Source–Load Scenario Construction Method for Rural Energy System},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/25771},
ISSN = {1546-0118},
ABSTRACT = {With the advancement of the Rural Revitalization Strategy and the “Dual Carbon” goals, rural energy systems are exhibiting pronounced multi-energy coupling, a high penetration of renewable energy, and strong load randomness, placing higher demands on the construction of source-load scenarios across multiple time scales. Addressing the limitations of traditional statistical models in generating high-quality short-term source-load scenarios and the tendency of deep learning methods to overlook medium- to long-term seasonal evolution patterns, this paper proposes a hybrid data- and model-driven method for constructing multi-energy source-load scenarios in rural systems. This method establishes a multi-time-scale generation framework encompassing daily scenarios, monthly scenarios, and a full-year 8760-h annual scenario. The proposed approach begins with data preprocessing to form a high-quality sample set. Subsequently, it utilizes a Wasserstein Generative Adversarial Network with Gradient Penalty model (WCGAN-GP) integrated with a temporal attention mechanism to achieve high-fidelity short-term generation of source-load profiles, including photovoltaic, small hydropower, electrical load, and thermal load. Furthermore, it characterizes seasonal evolution features by combining Dynamic Time Warping K-Medoids (DTW–K-Medoids) for typical day extraction and monthly Markov transition modeling. Finally, the annual scenario is continuously spliced and generated through a cross-month boundary cost function and dynamic programming path optimization. Results demonstrate that the short-term scenarios perform excellently across various evaluation metrics, while the monthly and annual scenarios accurately reflect seasonal variations and long-term trends, validating the accuracy and applicability of the proposed method.},
DOI = {10.32604/ee.2026.077169}
}