Machine learning autoencoder‐based parameters prediction for solar power generation systems in smart grid

Ahsan Zafar, Yanbo Che, Muhammad Faheem, Muhammad Abubakar, Shujaat Ali, Muhammad Shoaib Bhutta

IET Smart Grid 2024 article cited by 38

Source Open-access PDF More in Engineering & Materials

importance 5/10 partially solved Solar Radiation and Photovoltaics grid forecasting stage: research

Problem

Accurate prediction of solar power plant parameters is needed to minimise losses when traditional grids convert to smart grids.

Why it matters

Inaccurate parameter prediction reduces power generation capacity as energy systems modernise.

What the AI did

Used an LSTM model to predict solar plant parameters, then improved it with CNN-LSTM and a proposed autoencoder-LSTM, comparing the models.

Results

not stated in source

Impact

Evidence supports only a model comparison; abstract truncates before findings and no deployment stated.

Record

Venue	IET Smart Grid
Published	2024
Work type	article
Authors	Ahsan Zafar, Yanbo Che, Muhammad Faheem, Muhammad Abubakar, Shujaat Ali, Muhammad Shoaib Bhutta
Citations	38 (OpenAlex, at import time)
References	32
Model or method	LSTM, CNN-LSTM, autoencoder-LSTM
Organization	not stated in source
Primary source	https://doi.org/10.1049/stg2.12153
Open access	gold
Keywords	solar power, smart grid, LSTM, autoencoder, parameter prediction

Importance rating

5 / 10

real but narrower advance

Accurate solar plant parameter prediction to minimize losses in smart-grid conversion; tied to energy modernization but the demonstrated problem is parameter prediction for one plant, no scale stated.

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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