Afsaneh Mollasalehi | Searcharxiv

arXiv2026

Solar Cars: A Comprehensive Review

Energy crisis has forced many countries to think of a replacement for energy supply. Renewable energy sources as firendly environment sources play a pivotal role in producing clean energy for various sectors in industry. Gas emissions originating from the transportation industry is another contributing factor to air pollution. Hence, designing and utilizing vehicles that run on renewable energy is crucial, as it provides a dependable energy source that is naturally abundant, leaves nearly no carbon footprint, and is sustainable. Solar powered electric cars make a significant impact on global climate change. To better understand this impact and building upon the plenty of research done on this topic, this paper aims to provide a comprehensive review of the various factors related to solar cars. Specifically, this review will examine the following key factors: Types and sizing of solar cars, solar vehicle power source configurations, leading solar car nations, and solar car challenges.

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arXiv2025

Solar and Wind Power Forecasting: A Comparative Review of LSTM, Random Forest, and XGBoost Models

Rising global energy demand from population growth raises concerns about the sustainability of fossil fuels. Consequently, the energy sector has increasingly transitioned to renewable energy sources like solar and wind, which are naturally abundant. However, the periodic and unpredictable nature of these resources pose significant challenges for power system reliability. Accurate forecasting is essential to ensure grid stability and optimize energy management. But due to the high variability in weather conditions which directly affected wind and solar energy, achieving precise predictions remains difficult. Advancements in Artificial Intelligence (AI), particularly in Machine Learning (ML) and Deep Learning (DL), offer promising solutions to improve forecasting accuracy. The study highlights three widely used algorithms for solar and wind energy prediction: Long Short-Term Memory (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). These models are capable of learning complex patterns from historical and environmental data, enabling more accurate forecasts and contributing to the enhanced efficiency and reliability of renewable energy systems. This review aims to provide an overview on RF, XGBoost, and LSTM by conducting a comparative analysis across three essential criteria: research prevalence, model complexity, and computational execution time.

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