Samira Marhraoui^1,2,*, Basma Saad³, Hassan Silkan¹, Said Laasri², Asmaa El Hannani³

Energy Engineering, Vol.122, No.7, pp. 2653-2672, 2025, DOI:10.32604/ee.2025.064498 - 27 June 2025

Abstract Photovoltaic (PV) power forecasting is essential for balancing energy supply and demand in renewable energy systems. However, the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation. This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies: Hybrid-Si, Mono-Si, and Poly-Si, across three forecasting horizons: 1-step, 12-step, and 24-step. Among the tested models, the Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) architecture exhibited superior performance, particularly for the 24-step horizon, achieving R² = 0.9793 and MAE = 0.0162 for More >

Alex Ximenes Naves¹, Gladys Maquera², Assed Haddad¹, Dieter Boer^3,*

Energy Engineering, Vol.122, No.7, pp. 2611-2636, 2025, DOI:10.32604/ee.2025.061971 - 27 June 2025

Abstract The growing concern for energy efficiency and the increasing deployment of intermittent renewable energies has led to the development of technologies for capturing, storing, and discharging energy. Supercapacitors can be considered where batteries do not meet the requirements. However, supercapacitors in systems with a slower charge/discharge cycle, such as photovoltaic systems (PVS), present other obstacles that make replacing batteries more challenging. An extensive literature review unveils a knowledge gap regarding a methodological comparison of batteries and supercapacitors. In this study, we address the technological feasibility of intermittent renewable energy generation systems, focusing on storage solutions… More > Graphic Abstract

Gökhan Şahin^1,*, Ihsan Levent², Gültekin Işık², Wilfried van Sark¹, Sabir Rustemli³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1215-1248, 2025, DOI:10.32604/cmes.2025.063193 - 11 April 2025

Abstract This research investigates the influence of indoor and outdoor factors on photovoltaic (PV) power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency. To predict plant efficiency, nineteen variables are analyzed, consisting of nine indoor photovoltaic panel characteristics (Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Maximum Voltage (Umpp), Maximum Current (Impp), Filling Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), Module Temperature) and ten environmental factors (Air Temperature, Air Humidity, Dew Point, Air Pressure, Irradiation, Irradiation Propagation, Wind Speed, Wind… More >

Lingbao Wang^1,2, Zhi Gan², Zuowei Yang^3,*, Huashan Li^1,2, Yulie Gong^1,2, Xianbiao Bu^1,2

Energy Engineering, Vol.122, No.4, pp. 1443-1468, 2025, DOI:10.32604/ee.2025.060113 - 31 March 2025

Abstract Amidst the global push for decarbonization, solar-powered Organic Rankine Cycle (SORC) systems are gaining significant attention. The small-scale Organic Rankine Cycle (ORC) systems have enhanced environmental adaptability, improved system flexibility, and achieved diversification of application scenarios. However, the power consumption ratio of the working fluid pump becomes significantly larger relative to the total power output of the system, adversely impacting overall system efficiency. This study introduces an innovative approach by incorporating a vapor-liquid ejector into the ORC system to reduce the pump work consumption within the ORC. The thermo-economic models for both the traditional ORC… More >

Aws Al-Akam^1,*, Ahmed A. Abduljabbar², Ali Jaber Abdulhamed¹

Energy Engineering, Vol.121, No.12, pp. 3761-3777, 2024, DOI:10.32604/ee.2024.055198 - 22 November 2024

Abstract Photovoltaic (PV) panels are essential to the global transition towards sustainable energy, offering a clean, renewable source that reduces reliance on fossil fuels and mitigates climate change. High temperatures can significantly affect the performance of photovoltaic (PV) panels by reducing their efficiency and power output. This paper explores the consequential effect of various rooftop coverings on the thermal performance of photovoltaic (PV) panels. It investigates the relationship between the type of rooftop covering materials and the efficiency of PV panels, considering the thermal performance and its implications for enhancing their overall performance and sustainability. The… More >

Nataliya Shakhovska^1,2,*, Mykola Medykovskyi¹, Oleksandr Gurbych^1,3, Mykhailo Mamchur^1,3, Mykhailo Melnyk¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3147-3163, 2024, DOI:10.32604/cmc.2024.056542 - 18 November 2024

Abstract The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability, reliability, and economic benefits. This study explores advanced machine learning (ML) and deep learning (DL) techniques for predicting solar energy generation, emphasizing the significant impact of meteorological data. A comprehensive dataset, encompassing detailed weather conditions and solar energy metrics, was collected and preprocessed to improve model accuracy. Various models were developed and trained with different preprocessing stages. Finally, three datasets were prepared. A novel hour-based prediction wrapper was introduced, utilizing external sunrise and sunset data to restrict… More >

Ibtissam El Aouni, Hicham Labrim, Elhoussaine Ouabida, Ahmed Ait Errouhi, Rachid El Bouayadi, Driss Zejli, Aouatif Saad^*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.10, pp. 2323-2335, 2024, DOI:10.32604/fdmp.2024.050611 - 23 September 2024

Abstract The perfect combination of renewable energy and desalination technologies is the key to meeting water demands in a cost-effective, efficient and environmentally friendly way. The desalination technique by humidification-dehumidification is non-conventional approach suitable for areas with low infrastructure (such as rural and decentralized regions) since it does not require permanent maintenance. In this study, this technology is implemented by using solar energy as a source of thermal power. A seawater desalination unit is considered, which consists of a chamber with two evaporators (humidifiers), a wetted porous material made of a corrugated cellulose cardboard and a… More >

Jinglong Wang, Lin Lu^*, Kai Jiao

Energy Engineering, Vol.121, No.10, pp. 2681-2718, 2024, DOI:10.32604/ee.2024.051051 - 11 September 2024

Abstract Thermoelectric generators (TEGs) play a critical role in collecting renewable energy from the sun and deep space to generate clean electricity. With their environmentally friendly, reliable, and noise-free operation, TEGs offer diverse applications, including areas with limited power infrastructure, microelectronic devices, and wearable technology. The review thoroughly analyses TEG system configurations, performance, and applications driven by solar and/or radiative cooling, covering non-concentrating, concentrating, radiative cooling-driven, and dual-mode TEGs. Materials for solar absorbers and radiative coolers, simulation techniques, energy storage management, and thermal management strategies are explored. The integration of TEGs with combined heat and power More >

Djeldjli Halima^1,*, Benatiallah Djelloul¹, Ghasri Mehdi², Tanougast Camel³, Benatiallah Ali⁴, Benabdelkrim Bouchra¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4725-4740, 2024, DOI:10.32604/cmc.2024.051002 - 20 June 2024

Abstract When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the… More > Graphic Abstract

Yosr Laatiri, Habib Sammouda, Fadhel Aloulou^*

Journal of Renewable Materials, Vol.12, No.4, pp. 771-798, 2024, DOI:10.32604/jrm.2024.047022 - 12 June 2024

Abstract This article aims to present the feasibility of storing thermal energy in buildings for solar water heating while maintaining the comfort environment for residential buildings. Our contribution is the creation of insulating composite panels made of bio-based phase change materials (bio-PCM is all from coconut oil), cement and renewable materials (treated wood fiber and organic clay). The inclusion of wood fibers improved the thermal properties; a simple 2% increase of wood fiber decreased the heat conductivity by approximately 23.42%. The issues of bio-PCM leakage in the cement mortar and a roughly 56.5% reduction in thermal… More > Graphic Abstract