Volume 68

AI-Assisted Behavior-Informed Rural Load Representation and Demand-Side Scenario Analysis under Fragmented Data Mingxu Lyu, Yemao Zhang, Dan Chen, Di Wu, Weiwei Wang, Yanxin You, Fei Wang

Abstract

In energy systems, load curves are where natural electricity demand meets power-system supply. Under rural energy-transition policies in China, village collectives, local governments, and cooperatives are showing stronger willingness to participate in energy-transition planning, while local electricity-use patterns are being reshaped by distributed photovoltaics, electric-vehicle adoption, and changing seasonal demand. However, user-side information usable for constructing load profiles is often fragmented across actors and difficult to translate directly into power-system planning variables. This paper proposes an AI-assisted workflow for non-utility actors to generate reviewable residential living-load representations from fragmented rural evidence. Questionnaires, scenario-recall responses, appliance records, and electricity bills are first assigned source-based credibility weights and then structured by large language models into three auditable input libraries. Appliance-attribute-based trigger rules are used in a bottom-up Monte Carlo simulation to generate K=300 residential living-load candidates. Weak validation using limited local references — transformer capacity, seasonal electricity-use benchmarks, and intraday-shape plausibility checks — selects a representative baseline profile with bounded plausibility. Applied to a representative rural transformer area in central China, the workflow produces an interpretable residential living-load representation with clear seasonal patterns and morning-evening peaks. Illustrative S0/S1/S2 scenario traces further show that stated response willingness under time-of-use information and direct-incentive assumptions can shift selected schedulable loads toward midday and slightly improve local PV supply-to-load alignment. However, residential-side flexibility remains limited and cannot by itself substantially reshape transformer-area source-load matching.

Keywords Rural microgrid planning, Behavior-informed load profile generation, Fragmented user-side data, Planning-grade load representation, Demand-side scenario analysis