Probabilistic Forecasts of Global Horizontal Irradiance for Solar Systems

Jonler, Jonathan Fjord; BrunoLottrup, Frederik; Berg, Bogi; Zhang, Dalin; Chen, Kaixuan

doi:10.1109/lsens.2022.3228783

Cited by 8 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• application of autoregressive models for forecasting over very short planning horizons (an hour or less ahead) [9][10][11][12][13][14]; • use of probability distributions to determine the parameters of solar radiation and wind speed, followed by taking into account the efficiency of installations, the slope of solar panels and the orientation of wind turbines [15][16][17][18][19]; • application of forecasting models based on machine learning algorithms for direct generation forecast [20][21][22][23][24][25][26].…”

mentioning

confidence: 99%

Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage

et al. 2023

View full text Add to dashboard Cite

At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the installed capacity utilization factor, which is extremely important for optimally placing power facilities based on renewable energy sources and for the successful development of renewable energy. Efficient placement maximizes the installed capacity utilization factor of a power facility, increasing energy efficiency and the payback period. The installed capacity utilization factor depends on the assumed meteorological factors relating to geographical location and the technical characteristics of power generation. However, the installed capacity utilization factor cannot be accurately predicted, since it is necessary to know the volume of electricity produced by the power facility. A novel approach to the optimization of placement of renewable energy source power plants and their capacity factor forecasting was proposed in this article. This approach combines a machine learning forecasting algorithm (random forest regressor) with a metaheuristic optimization algorithm (grey wolf optimizer). Although the proposed approach assumes the use of only open-source data, the simulations show better results than commonly used algorithms, such as random search, particle swarm optimizer, and firefly algorithm.

show abstract

mentioning

confidence: 99%

Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Significant contributions have also been seen from models developed based on the probabilistic forecasting principles. There, the impact of uncertainties for long-term forecasting horizons was included to further improve the accuracy of the long-term forecasting for renewable energy-based systems [60][61][62][63]. Moreover, several studies deal with the impact of forecast uncertainty on the optimum power system sizing results [50,64].…”

Section: Power Electronics Mission Profile Specifications For Long -T...mentioning

confidence: 99%

Reliability-Oriented Design of Power Electronics-Based Power Systems

Sandelic

View full text Add to dashboard Cite

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research. -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal -

show abstract