Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4145 - 4145

Published: Aug. 20, 2024

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling grid management. This paper presents a comprehensive review conducted with reference to pioneering, comprehensive, data-driven framework proposed solar Photovoltaic (PV) generation prediction. systematic integrating comprises three main phases carried out by seven modules addressing numerous practical difficulties the task: phase I handles aspects related data acquisition (module 1) manipulation 2) in preparation development scheme; II tackles associated model 3) assessment its accuracy 4), including quantification uncertainty 5); III evolves towards enhancing incorporating context change detection 6) incremental learning when new become available 7). adeptly addresses all facets PV prediction, bridging existing gaps offering solution inherent challenges. By seamlessly these elements, our approach stands as robust versatile tool precision real-world applications.

Language: Английский

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Optimization of Microgrid Dispatching by Integrating Photovoltaic Power Generation Forecast

Sustainability, Journal Year: 2025, Volume and Issue: 17(2), P. 648 - 648

Published: Jan. 15, 2025

In order to address the impact of uncertainty and intermittency a photovoltaic power generation system on smooth operation system, microgrid scheduling model incorporating forecast is proposed in this paper. Firstly, factors affecting accuracy prediction are analyzed by classifying data using cluster analysis, analyzing its important features Pearson correlation coefficients, downscaling high-dimensional PCA. And based theories sparrow search algorithm, convolutional neural network, bidirectional long- short-term memory combined SSA-CNN-BiLSTM established, attention mechanism used improve accuracy. Secondly, multi-temporal dispatch optimization which aims at economic cost minimization environmental cost, constructed results. Further, differential evolution introduced into QPSO algorithm solved improved quantum particle swarm algorithm. Finally, feasibility forecasting model, as well validity solution algorithms, verified through real case simulation experiments. The results show that paper has high terms strategy, method with lowest selected obtain an effective way interact main grid realize stable economically optimized system.

Language: Английский

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Enhanced PV Power Prediction Considering PM10 Parameter by Hybrid JAYA-ANN Model

Electric Power Components and Systems, Journal Year: 2024, Volume and Issue: 52(11), P. 1998 - 2007

Published: March 4, 2024

The demand for electrical energy is continuously increasing in these days, particularly due to advancements the industrial sector. This surge has underscored importance of seeking alternative sources, with solar emerging as a standout option its low investment costs and environmental friendliness. However, variability photovoltaic power production, influenced by meteorological data, necessitates accurate prediction methods. To enhance precision predictions, incorporating new parameters alongside existing data advantageous. In this regard, study explores impact particulate matter (PM10) parameter on using artificial neural network (ANN) model JAYA-ANN. Comparing results based root mean squared absolute percentage errors reveals that hybrid JAYA-ANN consistently outperforms ANN persistence models. Notably, PM10 proves be significant input forecasting daily power.

Language: Английский

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A method for predicting the morphology of single-track laser cladding layer based on SO-LSSVR

Materials Today Communications, Journal Year: 2024, Volume and Issue: 39, P. 108666 - 108666

Published: March 19, 2024

Language: Английский

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Day-ahead photovoltaic power prediction based on a hybrid gradient descent and metaheuristic optimizer

Sustainable Energy Technologies and Assessments, Journal Year: 2023, Volume and Issue: 57, P. 103309 - 103309

Published: June 1, 2023

Language: Английский

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An optimal siting and economically optimal connectivity strategy for urban green 5G BS based on distributed photovoltaic energy supply

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 301, P. 118043 - 118043

Published: Jan. 10, 2024

Language: Английский

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Prediction of emission characteristics of diesel/n-hexanol/graphene oxide blended fuels based on fast outlier detection-sparrow search algorithm-bidirectional recurrent neural network

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 187, P. 1076 - 1096

Published: May 10, 2024

Language: Английский

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Mixed-frequency fusion grey panel model for spatiotemporal prediction of photovoltaic power generation

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123055 - 123055

Published: April 1, 2025

Language: Английский

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Research on correction method of borehole response in slim hole array lateral logging based on PSO-BP hybrid model prediction

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 30, 2025

Language: Английский

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Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Energies, Journal Year: 2024, Volume and Issue: 17(11), P. 2606 - 2606

Published: May 28, 2024

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, characteristic variables time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, user’s delineated by introducing purpose transfer probabilities, thus establishing a comprehensive model. Secondly, improved Floyd algorithm employed to select optimal path, taking into account various factors including signal light status, speed, position starting ending sections. Moreover, approach multi-lane lane change following utilization cellular automata theory introduced. To establish simulation model, real-time energy consumption integrated with aforementioned techniques. Thirdly, minimum regret value leveraged conjunction other factors, driving purpose, station electricity price, parking cost, more, simulate decision-making process users regarding stations. Subsequently, an EV framework based on driven prices interaction coupled network information. Finally, paper conducts large-scale simulations analyze characteristics regional transportation East China typical power case studies, thereby validating feasibility method.

Language: Английский

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