Eakbodin Gedkhaw, Nantinee Soodtoetong^*

Energy Engineering, Vol.122, No.11, pp. 4349-4363, 2025, DOI:10.32604/ee.2025.068577 - 27 October 2025

Abstract This research aimed to study the efficiency of solar power system in controlling hydro-organic smart farming system in closed greenhouse by developing an off-grid system consisting of 450 W solar panel, MPPT charge controller, 500 W Pure Sine Wave inverter and 2150 Ah Deep Cycle batteries in series as 24 V system to supply power to automatic control devices, including temperature, humidity, pH sensor and water pump in NFT (Nutrient Film Technique) hydroponic system using organic nutrient solution. The test result between 08:00–17:00 or 30 days found that the system can produce a maximum of… More > Graphic Abstract

Prasongsuk Songsree^*, Chaiyapon Thongchaisuratkrul^*

Energy Engineering, Vol.122, No.11, pp. 4541-4559, 2025, DOI:10.32604/ee.2025.068448 - 27 October 2025

Abstract The purpose of this research is to design and develop a demonstration Set of a water cooling system using a Peltier with solar energy and technology, and IoT (Internet of Things), and test and measure the performance of the Peltier Plate Water Cooling System Demonstration Set under different environmental conditions. To be used as a model for clean energy systems and experimental learning materials. The prototype system consists of a 100-W solar panel, a 12 V 20 Ah battery, a Peltier plate, a DS18B20 sensor, and a NodeMCU microcontroller. The system performance is determined by… More >

Reza Nadimi^1,*, Maryam Hashemi², Koji Tokimatsu³

Journal on Internet of Things, Vol.7, pp. 49-69, 2025, DOI:10.32604/jiot.2025.070822 - 30 September 2025

Abstract Existing Internet of Things (IoT) systems that rely on Amazon Web Services (AWS) often encounter inefficiencies in data retrieval and high operational costs, especially when using DynamoDB for large-scale sensor data. These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems. This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services. The proposed method employs AWS Lambda functions with Amazon Relational Database Service (RDS) to facilitate the transmission of data collected from temperature and humidity sensors to the… More >

Manoharan Madhiarasan^*

Energy Engineering, Vol.122, No.8, pp. 2993-3011, 2025, DOI:10.32604/ee.2025.068358 - 24 July 2025

Abstract Accurate Global Horizontal Irradiance (GHI) forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources. Particularly considering the implications of the aggressive GHG emission targets, accurate GHI forecasting has become vital for developing, designing, and operational managing solar energy systems. This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA (Autoregressive Integrated Moving Average), Elaman NN (Elman Neural Network), RBFN (Radial Basis Function Neural Network),… More >

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 >