Adaptive Load Forecasting for Renewable-Rich Power Systems: Techniques, Evaluation, and Deployment
Keywords:
Load forecasting, concept drift, demand response, distribution systems, federated learning, probabilistic forecasting, smart grids.Abstract
Renewable-rich distribution systems require load forecasts that may evolve over time with changing measurements and forecasts, operator input, tariffs, and control decisions. In this survey, that closed-loop task is treated as interactive load forecasting, with an overview of the literature published from 2015 to the present across different forecast horizons at residential, building, aggregator, and low-voltage feeder levels. The literature is organized according to a taxonomy based on representation, adaptation mechanism, uncertainty treatment, privacy architecture, and decision interface. Besides deployment evidence, deep temporal, graph, online, probabilistic, federated, and cost-oriented methods are compared. The main unresolved problem is the lack of causal consistency. The load being forecast is affected by activities pertaining to demand response, storage, and home-energy management. Consequently, there is a need for action-conditioned datasets, counterfactual assessments, calibrated online learning, and operational measures beyond mere average prediction error.
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