The increasing need for reducing carbon emissions and promoting smart, energy-saving
buildings is fueling the rising trend of sophisticated control systems. This study provides …

Aderibigbe, Ani, Ohenhen, Ohalete, & Daraojimba, P. No. 341-356 Page 342 of selecting
appropriate ML models based on criteria such as accuracy, adaptability to forecasting …

The energy industry worldwide is today confronted with several challenges, including
heightened levels of consumption and inefficiency, volatile patterns in demand and supply …

Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence
(AI) combined with Machine Learning (ML) has introduced a new era of remarkable …

Forecasting solar power production accurately is critical for effectively planning and
managing renewable energy systems. This paper introduces and investigates novel hybrid …

Bottom-up energy system models are often based on hourly time steps due to limited
computational tractability or data availability. However, in order to properly assess the …

The integration of renewable energy sources (RES) into smart grids has been considered
crucial for advancing towards a sustainable and resilient energy infrastructure. Their …

Accurately predicting the power produced during solar power generation can greatly reduce
the impact of the randomness and volatility of power generation on the stability of the power …

Solar energy is a widely accessible, clean, and sustainable energy source. Solar power
harvesting in order to generate electricity on smart grids is essential in light of the present …

In the context of escalating concerns about environmental sustainability in smart cities, solar
power and other renewable energy sources have emerged as pivotal players in the global …