Benchmark analysis of day-ahead solar power forecasting techniques using weather predictions

Lennard Visser, Tarek A. AlSkaif, Wilfried G.J.H.M. van Sark

Conference record of the IEEE Photovoltaic Specialists Conference 2019 conference-paper cited by 40

Source Open-access PDF More in Climate & Environment

importance 6/10 ai assisted Solar Radiation and Photovoltaics grid forecasting stage: research

Problem

Day-ahead prediction of power output of individual and aggregated PV systems is needed to keep the electricity grid reliable as solar penetration grows.

Why it matters

Inaccurate forecasting of intermittent PV production threatens grid stability and limits renewable energy adoption.

What the AI did

Different machine learning models were developed that autonomously predict day-ahead PV power production using historic production and regional weather predictions, evaluated across 152 PV systems.

Results

not stated in source

Impact

Peer-reviewed IEEE conference paper; abstract states accuracy was evaluated for 152 PV systems but gives no figures.

Record

Venue	Conference record of the IEEE Photovoltaic Specialists Conference
Published	2019
Work type	conference-paper
Authors	Lennard Visser, Tarek A. AlSkaif, Wilfried G.J.H.M. van Sark
Citations	40 (OpenAlex, at import time)
References	20
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1109/pvsc40753.2019.8980899
Open access	green
Keywords	machine learning, solar forecasting, photovoltaics, day-ahead prediction, grid stability

Importance rating

6 / 10

significant — thousands to millions affected

PV forecasting is needed for grid reliability as renewable penetration grows, affecting energy systems broadly; abstract cites 152 systems evaluated, no population scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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