Solar Power Forecasts

Visser, Lennard; Lorenz, Elke; Heinemann, Detlev; Sark, Wilfried van

doi:10.1016/b978-0-12-819727-1.00135-7

Cited by 3 publications

(14 citation statements)

References 53 publications

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“…Therefore, we will describe some general approaches to solar forecasting, differentiated by the method of data acquisition, as seen from different investigations in Figure 3a in Section 2. The most common ways to gather input data for determining atmo-spheric conditions are Satellite Images, All-Sky Imagers, Sensor Networks, and Numerical Weather Prediction [2,8,9,11]. Additional acronyms are added and can be found in the abbreviations, created by using Vosviewer (Version 1.6.18) [12].…”

Section: Solar Forecasting Methods and Performancementioning

confidence: 99%

“…In order to make forecasts of solar irradiance, consecutive images are used to determine Cloud Motion Vectors. Under the assumption that the cloud structures stay the same, the future position of the clouds can be determined [9,13]. Forecasting methods based on the mentioned approach generally have a good forecasting ability for a horizon of up to 6 h, but their performance tends to be less optimal in situations where clouds rapidly form and disperse [9,13].…”

Section: Satellite Imagesmentioning

confidence: 99%

“…ASIs are used for cloud detection, the determination of Cloud Motion, and the determination of cloud height [13]. ASIs can also be linked to other equipment as well, such as ceilometers or pyranometers [9]. Solar irradiance forecasts that are made by using ASIs generally use the following approach: (1) take sky pictures near or at the forecast site, (2) use image-processing techniques to detect clouds in the picture, (3) determine Cloud Motion Vectors through linking clouds in consecutive images, and (4) use Cloud Motion Vectors to determine future cloud positions and estimate the future irradiance accordingly [2,9].…”

Section: All-sky Imagersmentioning

confidence: 99%

“…ASIs can also be linked to other equipment as well, such as ceilometers or pyranometers [9]. Solar irradiance forecasts that are made by using ASIs generally use the following approach: (1) take sky pictures near or at the forecast site, (2) use image-processing techniques to detect clouds in the picture, (3) determine Cloud Motion Vectors through linking clouds in consecutive images, and (4) use Cloud Motion Vectors to determine future cloud positions and estimate the future irradiance accordingly [2,9]. ASI-based methods are able to provide forecasts with a very high spatial and temporal resolution, compared to satellite-based methods.…”

Section: All-sky Imagersmentioning

confidence: 99%

“…One of the proposed solutions is to forecast solar irradiance and, in turn, to forecast solar energy production to help balance supply and demand through combination with electricity storage [7,8]. As a result, solar forecasting has seen an increase in interest from researchers, grid operators, and other parties involved in the electricity market [9].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Solar Forecasting Methods and Performancementioning

confidence: 99%

Section: Satellite Imagesmentioning

confidence: 99%

Section: All-sky Imagersmentioning

confidence: 99%

Section: All-sky Imagersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Barhmi,

Heynen,

Golroodbari

et al. 2024

Solar

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Selection of the K Parameter in the K-Nearest Neighbor Algorithm for Solar Power Prediction

Yehor,

Kateryna

2023

2023 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo)

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Integration of the radial basis functional network and sliding mode control for the sunshine radiation forecast

Tsai,

Huang

2024

AIMSE

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<abstract> <p>In this paper, we propose a forecasting system of sunshine radiation for planners to quickly and accurately predict the output of solar power. The field data, including observation time, temperature, relational humidity, wind speed and global radiation, were collected, and the data clusters were embedded in the Excel Database. To improve the computational performance, the data selection technique was used in the stage of data cleaning, data integration and data reduction. Using the Integration of the Radial Basis Function Network (RBFN) and Sliding Mode Control (SMC), a Sliding Mode Radial Basis Function Network (SMRBFN) was proposed to solve this forecasting problem. Since the Sliding Mode Control has the design's sense of optimal parameters, three parameters in the SMRBFN were dynamically adjusted to promote the accurate and reliability of forecasting system. Linking the SMRBFN and Excel database, the learning stage and testing stage of SMRBFN retrieved the input data from Excel Database to perform and analyze the forecasting system. The proposed algorithm was tested on Kaohsiung district in summer and winter. The average prediction error of MAPE and RMSE obtained from the forecasting results are about 9% and 0.223, respectively. It can be proved that SMRBFN can efficiently forecast the sunshine radiation and accurately provide the output of solar power in an uncertainty environment.</p> </abstract>

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Solar Power Forecasts

Cited by 3 publications

References 53 publications

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

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

Selection of the K Parameter in the K-Nearest Neighbor Algorithm for Solar Power Prediction

Integration of the radial basis functional network and sliding mode control for the sunshine radiation forecast

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