Our analysis reveals a notable shift from statistical models toward machine-learning and deep-learning approaches, particularly from 2018 to 2022. Hybridization of models consistently
To handle this large amount of historical data efficiently, this paper proposed a solar PV power prediction model using big data tools. Meanwhile, ANN is a mathematical model which is
In this context, data analysis techniques in big data environment, mainly through machine learning (ML) and data mining (DM), may help the power sector to establish a new operating model,
The paper focuses on two primary aspects: short-term forecasting of photovoltaic power generation and the exploration of electric vehicle user clustering addressed using artificial intelligence.
The research establishes a foundation for improving homomorphic encryption, enhancing key management, and creating a big data security framework specific to photovoltaic energy production.
Explore how big data is revolutionising solar energy through predictive maintenance, real-time optimisation, and smarter forecasting—driving greater efficiency and sustainability.
By analyzing the daily electricity yielded, total electricity yielded, DC power, AC power, ambient temperature, module temperature, and irradiation of solar panels, we discovered their
Several PV forecasting methods based on machine learning algorithms (MLAs) have recently emerged. This paper presents machine learning methods for multi-label forecasting of PV
Big Data Analytics: With the increasing volume of data generated by solar power systems, big data analytics techniques (e.g. distributed computing, parallel processing, and scalable algorithms) allow
By investigating the most recent literature, this review identifies critical research gaps and suggests future directions for enhancing forecasting models, including improving model
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