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Updated: Sep 17, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Enhancing PV power forecasting through feature selection and artificial neural networks: a case study.

Mokhtar Ali¹, Abdelhalim Rabehi¹, Abdelkerim Souahlia¹

¹Telecommunications and Smart Systems Laboratory, University of Djelfa, P.O. Box 3117, 17000, Djelfa, Algeria.

Scientific Reports

|July 2, 2025

Summary

Feature selection significantly boosts photovoltaic power forecasting accuracy when combined with artificial neural networks like MLP and LSTM. This improves solar energy management and grid stability.

Keywords:

Artificial neural networks Features selection Forecasting PV power Renewable energy

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Published on: August 23, 2012

Area of Science:

Renewable Energy Systems
Artificial Intelligence in Energy
Data Science for Power Grids

Background:

Growing demand for reliable renewable energy forecasting.
Need for improved accuracy in photovoltaic (PV) power output prediction.
Challenges in managing solar energy due to its intermittent nature.

Purpose of the Study:

To enhance photovoltaic (PV) power forecasting accuracy.
To systematically integrate feature selection techniques with artificial neural networks (ANNs).
To identify the most relevant predictors for PV output.

Main Methods:

Employed feature selection methods: ReliefF, minimum correlation, Chi-square test.
Developed and tested two predictive models: Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks.
Utilized a real-world PV dataset from southern Algeria.

Main Results:

Feature selection significantly improved forecasting accuracy for both MLP and LSTM models.
ReliefF with MLP achieved a normalized Mean Absolute Error (nMAE) of 9.21% and R² of 0.9608.
Chi-square selected features with LSTM resulted in an nMAE of 9.29% and R² of 0.946.

Conclusions:

Careful feature selection enhances ANN model performance for PV forecasting.
Feature selection reduces model complexity and improves generalization capabilities.
Findings offer valuable insights for efficient solar energy management and grid stability.