Adaptive sliding mode control based on maximum power point tracking for boost converter of photovoltaic system under reference voltage optimizer DOI Creative Commons
Borhen Torchani, Ahmad Taher Azar, Anis Sellami

et al.

Frontiers in Energy Research, Journal Year: 2024, Volume and Issue: 12

Published: Oct. 17, 2024

This article presents an innovative APISMC method applied to PVS, integrating the MPPT technique for a boost converter. The primary objective of this approach is maximize converter’s output power while ensuring optimal operation in face varying environmental conditions such as solar irradiance and temperature, dynamically adapting variations system parameters, demonstrated by obtained results. To achieve this, RVO employed generate reference voltage power. A PI controller calculates current based on control modeling utilizes all its variables synthesize sliding surface duty cycle converter control. Simulations conducted demonstrate superior performance terms stability, speed, compared traditional algorithms. main contributions include improvement robustness against variations, thanks integration adaptive algorithm within SMC. Moreover, proposed theoretical practical framework enables rapid attainment adjusting real-time, optimizing maximum extraction stable regulation even under non-ideal conditions.

Language: Английский

A Multi-Step-Ahead Photovoltaic Power Forecasting Approach Using One-Dimensional Convolutional Neural Networks and Transformer DOI Open Access
Jihoon Moon

Electronics, Journal Year: 2024, Volume and Issue: 13(11), P. 2007 - 2007

Published: May 21, 2024

Due to environmental concerns about the use of fossil fuels, renewable energy, especially solar is increasingly sought after for its ease installation, cost-effectiveness, and versatile capacity. However, variability in factors poses a significant challenge photovoltaic (PV) power generation forecasting, which crucial maintaining system stability economic efficiency. In this paper, novel muti-step-ahead PV forecasting model by integrating single-step multi-step forecasts from various time resolutions was developed. One-dimensional convolutional neural network (CNN) layers were used capture specific temporal patterns, with transformer improving leveraging combined outputs CNN. This combination can provide accurate immediate as well ability identify longer-term trends. Using DKASC-ASA-1A 1B datasets empirical validation, several preprocessing methods applied series experiments conducted compare performance other widely deep learning models. The framework proved be capable accurately predicting multi-step-ahead at multiple resolutions.

Language: Английский

Citations

6
SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea DOI Open Access
Hyunsik Min, Seokjun Hong, Jeonghoon Song

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(11), P. 2071 - 2071

Published: May 27, 2024

We present SolarFlux Predictor, a novel deep-learning model designed to revolutionize photovoltaic (PV) power forecasting in South Korea. This uses self-attention-based temporal convolutional network (TCN) process and predict PV outputs with high precision. perform meticulous data preprocessing ensure accurate normalization outlier rectification, which are vital for reliable analysis. The TCN layers crucial capturing patterns energy data; we complement them the teacher forcing technique during training phase significantly enhance sequence prediction accuracy. By optimizing hyperparameters Optuna, further improve model’s performance. Our incorporates multi-head self-attention mechanisms focus on most impactful features, thereby improving In validations against datasets from nine regions Korea, outperformed conventional methods. results indicate that is robust tool systems’ management operational efficiency can contribute Korea’s pursuit of sustainable solutions.

Language: Английский

Citations

5
SolarNexus: A deep learning framework for adaptive photovoltaic power generation forecasting and scalable management DOI
Hyunsik Min, Byeongjoon Noh

Applied Energy, Journal Year: 2025, Volume and Issue: 391, P. 125848 - 125848

Published: April 11, 2025

Language: Английский

Citations

0
Advancements and Challenges in Photovoltaic Power Forecasting: A Comprehensive Review DOI Creative Commons
Paolo Di Leo, Alessandro Ciocia, Gabriele Malgaroli

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(8), P. 2108 - 2108

Published: April 19, 2025

The fast growth of photovoltaic (PV) power generation requires dependable forecasting methods to support efficient integration solar energy into systems. This study conducts an up-to-date, systematized analysis different models and used for prediction. It begins with a new taxonomy, classifying PV according the time horizon, architecture, selection criteria matched certain application areas. An overview most popular heterogeneous techniques, including physical models, statistical methodologies, machine learning algorithms, hybrid approaches, is provided; their respective advantages disadvantages are put perspective based on tasks. paper also explores advanced model optimization methodologies; achieving hyperparameter tuning; feature selection, use evolutionary swarm intelligence which have shown promise in enhancing accuracy efficiency models. review includes detailed examination performance metrics frameworks, as well consequences weather conditions affecting renewable operational economic implications performance. highlights recent advancements field, deep architectures, incorporation diverse data sources, development real-time on-demand solutions. Finally, this identifies key challenges future research directions, emphasizing need improved adaptability, quality, computational large-scale By providing holistic critical assessment landscape, aims serve valuable resource researchers, practitioners, decision makers working towards sustainable reliable deployment worldwide.

Language: Английский

Citations

0
Predictive Modeling of Photovoltaic Energy Yield Using an ARIMA Approach DOI Creative Commons
Fatima Sapundzhi,

Aleksandar Chikalov,

Slavi Georgiev

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(23), P. 11192 - 11192

Published: Nov. 30, 2024

This paper presents a method for predicting the energy yield of photovoltaic (PV) system based on ARIMA algorithm. We analyze two key time series: specific and total PV system. Two models are developed each one selected by authors determined SPSS. Model performance is evaluated through fit statistics, providing comprehensive assessment model accuracy. The residuals’ ACF PACF examined to ensure adequacy, confidence intervals calculated residuals validate models. A monthly forecast then generated both series, complete with intervals, demonstrate models’ predictive capabilities. results highlight effectiveness in forecasting yields, offering valuable insights optimizing planning. study contributes field renewable demonstrating applicability systems.

Language: Английский

Citations

1
Adaptive sliding mode control based on maximum power point tracking for boost converter of photovoltaic system under reference voltage optimizer DOI Creative Commons
Borhen Torchani, Ahmad Taher Azar, Anis Sellami

et al.

Frontiers in Energy Research, Journal Year: 2024, Volume and Issue: 12

Published: Oct. 17, 2024

This article presents an innovative APISMC method applied to PVS, integrating the MPPT technique for a boost converter. The primary objective of this approach is maximize converter’s output power while ensuring optimal operation in face varying environmental conditions such as solar irradiance and temperature, dynamically adapting variations system parameters, demonstrated by obtained results. To achieve this, RVO employed generate reference voltage power. A PI controller calculates current based on control modeling utilizes all its variables synthesize sliding surface duty cycle converter control. Simulations conducted demonstrate superior performance terms stability, speed, compared traditional algorithms. main contributions include improvement robustness against variations, thanks integration adaptive algorithm within SMC. Moreover, proposed theoretical practical framework enables rapid attainment adjusting real-time, optimizing maximum extraction stable regulation even under non-ideal conditions.

Language: Английский

Citations

0