Day-ahead Solar Power Plant Forecasting Accuracy Improvement on the Hourly Basis

Snegirev, Denis A.; Eroshenko, Stanislav A.; Khalyasmaa, Alexandra I.; Dubailova, Valeria V.; Степанова, А. И.

doi:10.1109/eiconrus.2019.8657024

Cited by 15 publications

(1 citation statement)

References 4 publications

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“…Within these methods, physical model-based approaches depend a lot on modeling and simulation of physical factors such as solar radiation, clouds, and atmospheric conditions, which are even highly dynamic during any short time periods. In comparison, statistical methods, presented in most current works, build data-driven probabilistic models that can be based on historical data to predict short-term future solar energy production (Blaga et al, 2019;Snegirev et al, 2019;Han et al, 2022) either in long-term or short-term time period. Machine learning methods, especially deep learning techniques, are also playing an increasingly important role in solar energy prediction.…”

Section: Introductionmentioning

confidence: 99%

A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

Zhang,

Tang,

et al. 2024

Front. Energy Res.

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Renewable energy resources like solar power contribute greatly to decreasing emissions of carbon dioxide and substituting generators fueled by fossil fuels. Due to the unpredictable and intermittent nature of solar power production as a result of solar radiance and other weather conditions, it is very difficult to integrate solar power into conventional power systems operation economically in a reliable manner, which would emphasize demand for accurate prediction techniques. The study proposes and applies a revised radial basis function neural network (RBFNN) scheme to predict the short-term power output of photovoltaic plant in a day-ahead prediction manner. In the proposed method, the linear as well as non-linear variables in the RBFNN scheme are efficiently trained using the whale optimization algorithm to speed the convergence of prediction results. A nonlinear benchmark function has also been used to validate the suggested scheme, which was also used in predicting the power output of solar energy for a well-designed experiment. A comparison study case generating different outcomes shows that the suggested approach could provide a higher level of prediction precision than other methods in similar scenarios, which suggests the proposed method can be used as a more suitable tool to deal such solar energy forecasting issues.

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Section: Introductionmentioning

confidence: 99%

A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

Zhang,

Tang,

et al. 2024

Front. Energy Res.

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A rapid prediction model of photovoltaic power generation for autonomous long‐duration aerostat

Liu

et al. 2023

IET Renewable Power Gen

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Autonomous long‐duration aerostats (LDA) are one of the most popular research directions of high‐altitude platforms (HAPS) in recent years. Solar photovoltaic (PV) array is the energy source of autonomous long‐duration aerostat, whose power generation predicting accuracy and speed affect the subsequent flight control strategy. Limited by incompleteness cognition of near space, current predicting results cannot meet the requirements of autonomous LDA. In this paper, a novel rapid prediction model of the PV array is proposed. Based on spatial position relation of PV cells, this model can predict the power of single PV cell in any state. The four influence factors including time difference, angle, efficiency loss and temperature are analyzed and optimized comprehensively and innovatively. The new model can be applied in both static state aerostat and dynamic state aerostat. Compared with the traditional model, the efficiency can be improved by 50% and the prediction accuracy can be improved by 10%.

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Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

Prema¹,

Bhaskar

Almakhles

et al. 2022

IEEE Access

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Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices.

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Day-ahead Solar Power Plant Forecasting Accuracy Improvement on the Hourly Basis

Cited by 15 publications

References 4 publications

A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

A rapid prediction model of photovoltaic power generation for autonomous long‐duration aerostat

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

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