Example Of Solar Energy Forecasting

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Due to the stochastic fluctuant characteristic of solar irradiance, large-scale grid-connected photovoltaic (PV) power plant can bring great difficulties to the operation of power system. One feasible way to solve this problem is PV power forecasting. In this paper, a multi-level wavelet decomposition based day-ahead solar irradiance forecasting method is proposed in this paper. First, the daily solar irradiance series are classified into different patterns according to weather conditions. Then for each weather pattern, the solar irradiance of the next day 24 hours is forecasted using decomposed data series at different WD level. Then a data-driven fusion model corresponding to the weather pattern is applied to fuse the predictions into…show more content…
However, taking into account the economic and feasibility factors, PV power forecasting is still one of the most effective and economical ways to solve the uncertainty and fluctuation problems in PV system. Day-ahead PV power forecasting can provide the expected future PV power output of the next day 24 hours, which is an important reference information for power generation planning and system scheduling [7]. Accurate solar forecasting is not only able to help system operators and planners to better manage the variability and uncertainty of PV power in advance, but also can benefit PV plant managers as they avoid possible penalties that are incurred due to deviations between forecasted and produced energy [8], [9]. Solar irradiance is the main influence factor that affecting the power output of a PV plant, the accurate forecasting of irradiance is of great importance for step-wise PV power forecasting. Affected by the micro-meteorological environment of PV power plant, solar irradiance data series contains both stable, periodic part and fluctuant, random part. The former part daily changed with time and is more obvious on sunny days, while the latter part is mainly affected by meteorological factors such as cloud movement or showers. The proportion of the two parts also varies with different weather conditions. In cloudy or rainy days, the variability of original data series increases
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