Enhancing Solar Power Efficiency: Smart Metering and ANN-Based Production Forecasting

This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is co...

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Bibliographic Details
Published in:Computers (Basel) 2024-09, Vol.13 (9), p.235
Main Authors: Ledmaoui, Younes, El Fahli, Asmaa, El Maghraoui, Adila, Hamdouchi, Abderahmane, El Aroussi, Mohamed, Saadane, Rachid, Chebak, Ahmed
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Architecture
Artificial intelligence
Artificial neural networks
Comparative studies
Data analysis
Design
Efficiency
Electricity
Energy consumption
Energy utilization
Forecasting
Irradiance
Machine learning
Measuring instruments
monitoring
Monitoring systems
Neural networks
Performance evaluation
Performance prediction
Photovoltaic cells
Power
Power efficiency
Prediction models
R&D
Recurrent neural networks
Renewable resources
Research & development
Scientific visualization
Simulation
smart city
Smart grid
smart meter
Software
Solar energy
Solar panels
Temperature
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Summary:This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is conducted using a smart metering device designed for a photovoltaic system at an industrial site in Benguerir, Morocco. The smart metering device collects energy usage data from a submeter and transmits it to the cloud via an ESP-32 card, enhancing monitoring, efficiency, and energy utilization. Our methodology includes an analysis of solar resources, considering factors such as location, temperature, and irradiance levels, with PVSYST simulation software version 7.2, employed to evaluate system performance under varying conditions. Additionally, a data logger is developed to monitor solar panel energy production, securely storing data in the cloud while accurately measuring key parameters and transmitting them using reliable communication protocols. An intuitive web interface is also created for data visualization and analysis. The research demonstrates a holistic approach to smart metering devices for photovoltaic systems, contributing to sustainable energy utilization, smart grid development, and environmental conservation in Morocco. The performance analysis indicates that ANNs are the most effective predictive model for solar energy forecasting in similar scenarios, demonstrating the lowest RMSE and MAE values, along with the highest R2 value.
ISSN:2073-431X
2073-431X
DOI:10.3390/computers13090235