Solar forecasting with hourly updated numerical weather prediction

Zhang, Gang; Yang, Di; Galanis, George; Androulakis, Emmanouil

doi:10.1016/j.rser.2021.111768

Cited by 53 publications

(12 citation statements)

References 60 publications

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“…For the ensemble forecasting, there are two topics, namely, solar power forecasting and solar irradiance forecasting which are known as solar forecasting. Meanwhile, they have strongly correlated each other and cannot be separated [ 11 ]. It should be noted that there is a strong impact of solar irradiance on the accuracy of solar power forecasts for the power production systems of various sorts.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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“…One of the recent developments is an emphasis on improving solar irradiation prediction by NWP models [13]. For example, Zhang et al [24] researched the effect of postprocessing NWP model outputs for solar forecasting. However, when comparing their method to time series modeling and extrapolation techniques, they found only marginally small improvements.…”

Section: Numerical Weather Predictionsmentioning

confidence: 99%

“…Satellite-based approaches, such as the FengYun-4 geostationary satellite data, have been utilized to develop solar radiation nowcasting systems [73]. Additionally, Numerical Weather Prediction has been employed to enhance the solar power forecasting accuracy by considering weather conditions [24]. Machine learning algorithms like the Long Short-Term Memory (LSTM) Neural Network have been utilized for short-term solar power forecasting [91].…”

Section: Prevalence Of Artificial Intelligencementioning

confidence: 99%

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Barhmi,

Heynen,

Golroodbari

et al. 2024

Solar

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Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape.

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“…One of the main challenges is the accuracy of weather forecasts. Weather is inherently unpredictable, and even the best weather forecasts can be subject to errors [61]. Another challenge is the integration of weather forecast data with the energy management system (EMS) of the microgrid.…”

Section: Challengesmentioning

confidence: 99%

Microgrid Energy Management using Weather Forecasts: Case Study, Discussion and Challenges

Akter,

Islam,

Hasan

2023

IJRCS

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The main objective of this study is to demonstrate the integration of weather forecasts which can lead to a significant reduction in energy costs and carbon emissions while ensuring the reliability of the microgrid operation. By serving a small area or a particular building, the incorporation of weather forecasts can considerably increase the efficiency of microgrid energy management. The planning and operation of microgrids can be greatly improved by using weather predictions, which give useful information about upcoming weather conditions. By forecasting future energy demand and supply based on meteorological conditions, Microgrid Energy Management (MEM) is utilized to optimize the energy management decisions in microgrid systems. Making better choices regarding energy generation, storage, and consumption may be aided by the incorporation of weather forecasts, which can offer a more precise and trustworthy estimate of the energy demand and supply. This strategy can result in increased energy efficiency, decreased energy prices, and decreased carbon emissions, all of which are important goals for contemporary power systems. A promising approach for raising energy effectiveness and lowering greenhouse gas emissions in contemporary power networks is MEM. The incorporation of weather forecasts into MEM can improve decision-making regarding energy management by giving a better insight of future energy demand and supply. This essay examines the advantages and disadvantages of using weather forecasts in MEM through the presentation of a case example. By providing valuable information about future weather conditions, weather forecasts this review explain the Optimized Renewable Energy Integration, Improved Energy Storage Utilization, Load Shifting and Demand Response, Efficient Grid Management for reducing reliance on fossil fuels and lowering energy cost and carbon emissions. In order to address the issues related with MEM employing weather forecasts, this study offers potential fixes for increasing the accuracy of weather forecasts and emphasizes the necessity for more research in this area.

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Solar forecasting with hourly updated numerical weather prediction

Cited by 53 publications

References 60 publications

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Microgrid Energy Management using Weather Forecasts: Case Study, Discussion and Challenges

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