Algorithmic realization of short-term solar power plant output forecasting

Snegirev, Denis A.; Eroshenko, Stanislav A.; Valiev, Rustam; Khalyasmaa, Alexandra I.

doi:10.1109/ctsys.2017.8109532

Cited by 23 publications

(10 citation statements)

References 9 publications

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“…The solar power plant generation forecast is based on the calculation model of solar radiation intensity at the power plant territory [5]. Calculation of the solar radiation incident on the photovoltaic panel surface includes the determination of three values, which are solar radiation on the boundary of the earth's atmosphere, solar radiation on the horizontal surface of the earth and solar radiation on the inclined surface [6][7]. The value of solar radiation on the boundary of the earth's atmosphere is calculated from the geometric model of the Sun's motion.…”

Section: Solar Power Plant Output Forecastmentioning

confidence: 99%

“…During the foreign experience analysis the impossibility of MAPE error evaluation criteria in a traditional form using for the statistical evaluation of the significance and comparative analysis of the solar energy forecasting model efficiency was established. The solar power plant installed power is assumed as a value, in relation to which the generation forecast MAPE error is calculated [7].…”

Section: Forecast Error Evaluationmentioning

confidence: 99%

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Solar power generation short-term forecasting model’s implementation experience

Kochneva

2018

MATEC Web Conf.

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Recently there is significant increase in the installed capacity of solar power plants in Russia. Thereby there are issues of solar power plants owners information support for participation in wholesale electricity market. The paper describes the experience of short term forecasting system practical implementation. The system is proposed for forecasting the solar power plant generation “a day ahead” as a part of the software for automatic meter reading systems “Energosfera”. The short-term forecasting program modules structure, key parameters and characteristics used during the forecasting process description is presented.

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Section: Solar Power Plant Output Forecastmentioning

confidence: 99%

Section: Forecast Error Evaluationmentioning

confidence: 99%

Solar power generation short-term forecasting model’s implementation experience

Kochneva

2018

MATEC Web Conf.

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“…The algorithm for calculating the short-term forecast is practically implemented in three stages [5][6][7]: 1.…”

Section: Solar Energy Plant Generation "A Day Ahead" Forecastmentioning

confidence: 99%

“…Meteorological condition short-term forecasting in the territory of Russian Federation is carried out in accordance with the current governing documents. For example, according to the national guidelines in the firld of meteorology the cloudness forecast should correspond to the 8-point scale (step 0,125 pu) [7]. However according to international standards the scale for the same variable should be divided into 10 points (step 0,100 pu).…”

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confidence: 99%

Solar Power Plant Generation Short-Term Forecasting Model

et al. 2018

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Recently, renewable generation plays an increasingly important role in the energy balance. Solar energy is developing at a rapid pace, while the solar power plants output depends on weather conditions. Solar power plant output short-term forecasting is an urgent issue. The future electricity generation qualitative forecasts allow electricity producers and network operators to actively manage the variable capacity of solar power plants, and thereby to optimally integrate the solar resources into the country's overall power system. The article presents one of the possible approaches to the solution of the short-term forecasting problem of a solar power plant output.

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“…There are a number of outstanding methodologies based on the identification of satellites images of cloud movement in the area where the object under consideration is located [2].…”

Section: Introductionmentioning

confidence: 99%

Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology

2018

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Abstract. In conditions of development of generating facilities on renewable energy sources, the technology runs up to uncertainty in the operational and short-term planning of the power system operating modes. To date, reliable tools for forecasting the generation of solar power stations are required. This paper considers the methodology of operational forecasting of solar power stations output based on the mathematical apparatus of cubic exponential smoothing with trend and seasonal components. The presented methodology was tested based on the measuring data of a real solar power station. The average forecast error was not more than 10% for days with variable clouds and not more than 3% for clear days, which indicates the effectiveness of the proposed approach.

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Algorithmic realization of short-term solar power plant output forecasting

Cited by 23 publications

References 9 publications

Solar power generation short-term forecasting model’s implementation experience

Solar power generation short-term forecasting model’s implementation experience

Solar Power Plant Generation Short-Term Forecasting Model

Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology

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