An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting

Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records. This makes standard day-ahead forecasting difficult: persistence and physical baselines can be sensitive to calibration and timestamp alignment, while single machine-learning models may capture only one structure in the data and overstate skill under non-temporal validation. We study this problem at a United Kingdom charging-station site, where PV forecast

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ethics.ai (3 August 2026), “An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting,” evidence record 15885, https://ethics.ai/record/15885 (originally published by arXiv).

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