Machine learning-assisted evaluation of PVSOL software using a real-time rooftop PV system: a case study in Kocaeli, Turkey, with a focus on diffuse solar radiation

International Journal of Low-Carbon Technologies, Journal Year: 2025, Volume and Issue: 20, P. 223 - 233

Published: Jan. 1, 2025

Abstract Reducing energy-related CO2 emissions is vital for global climate targets, with Net Zero Energy Buildings (NZEBs) playing a key role. This study evaluates PVSOL software’s accuracy in simulating rooftop photovoltaic (PV) system an NZEB Kocaeli, Turkey. A machine learning model enhanced result reliability using local weather data. The system’s first-year performance ratio was 81.9%, close to the theoretical 84.53%. 435 600 USD investment expected be recovered 11.42 years, while predicts 14.9 years. findings confirm PVSOL’s PV systems, emphasizing their effectiveness reduction and energy transition efforts.

Language: Английский

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Artificial Intelligence-Based Improvement of Empirical Methods for Accurate Global Solar Radiation Forecast: Development and Comparative Analysis

Energies, Journal Year: 2024, Volume and Issue: 17(17), P. 4302 - 4302

Published: Aug. 28, 2024

Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly the use of artificial neural networks (ANNs) as most widely used technique. To address shortage solar measurement various places worldwide, several radiation methods have been developed to forecast global (GSR). With this consideration, study aims develop temperature-based GSR models using a commonly utilized approach machine learning techniques, ANNs, predict just temperature data. It also compares performance these empirical Additionally, it develops precise for five new sites entire region, which currently lacks AI-based despite presence proposed plants area. The examines impact varying lengths validation datasets on models’ prediction accuracy, received little attention. Furthermore, investigates different ANN architectures estimation introduces comprehensive comparative study. findings indicate that advanced both accurately GSR, with coefficient determination, R2, values ranging from 96% 98%. Moreover, local general formulas model exhibit comparable at non-coastal sites. Conversely, ANN-based perform almost identically, high ability any location, even during winter months. fewer neurons their single hidden layer generally outperform those more. efficacy precision models, ones, are minimally impacted by size data sets. This reveals was significantly influenced weather conditions such clouds rain, especially coastal In contrast, were less variations, approximately 7% better than ones best-developed thus highly recommended. They enable rapid is useful design evaluation continuously easily recorded purposes.

Language: Английский

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A Prediction of the Monthly Average Daily Solar Radiation on a Horizontal Surface in Saudi Arabia Using Artificial Neural Network Approach

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1149 - 1149

Published: April 10, 2025

When planning a solar energy conversion system, having sufficiently reliable values of the monthly average daily radiation (MADSR) on horizontal surface is essential. Traditionally, estimates based other climatological variables for which more information available have been relied upon to compensate lack direct measurements. Solar varies widely, requires creation site-specific forecast models. By using artificial neural network (ANN) models or similar methods historical datasets, can be easily assessed. To verify validity established ANN model, series analyses was performed mean squared error, coefficient determination (R2), and absolute error. The study used dataset collected from nine weather stations in Saudi Arabia 1985 2000. input parameters model were maximum air relative humidity, latitude, ambient temperature, longitude, minimum sunshine duration, location altitude, corresponding month. R2 whole test 0.8449. Furthermore, sensitivity analysis showed that site elevation (location altitude) had most significant effect MADSR surface, with contribution value 14.66%. results show accurately surfaces regardless seasonal variations conditions. this work important not only its shape forecasting but also establishing practical application ANNs renewable management. will help improve utilization support sustainable efforts. proposed believed useful predicting locations climatic conditions sites. approach may functional basic strategy arrangement suitable meteorological data.

Language: Английский

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PM2.5 reduces the daytime/nighttime urban heat island intensity over mainland China

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106001 - 106001

Published: Nov. 1, 2024

Language: Английский

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Comparison of calculation methods and artificial neural network results in regional solar irradiation prediction

Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 26, 2024

Language: Английский

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