Abstract
With the increasing permeability of photovoltaic (PV) power production, the uncertainties and randomness of PV power have played a critical role in the operation and dispatch of the power grid and amplified the abandon rate of PV power. Consequently, the accuracy of PV power forecast urgently needs to be improved. Based on the amplitude and fluctuation characteristics of the PV power forecast error, a short-term PV output forecast method that considers the error calibration is proposed. Firstly, typical climate categories are defined to classify the historical PV power data. On the one hand, due to the non-negligible diversity of error amplitudes in different categories, the probability density distributions of relative error (RE) are generated for each category. Distribution fitting is performed to simulate probability density function (PDF) curves, and the RE samples are drawn from the fitted curves to obtain the sampling values of the RE. On the other hand, based on the fluctuation characteristic of RE, the recent RE data are utilized to analyze the error fluctuation conditions of the forecast points so as to obtain the compensation values of the RE. The compensation values are adopted to sequence the sampling values by choosing the sampling values closest to the compensation ones to be the fitted values of the RE. On this basis, the fitted values of the RE are employed to correct the forecast values of PV power and improve the forecast accuracy.
Highlights
With the progressively prominent energy shortage and the deteriorating climate, the significance of renewable energy has reached new heights
Due to the effect of meteorological factors on PV power, a short-term PV power forecast model based on error calibration under typical climate categories is developed, applied to the day-head PV
To simulate the relative error (RE) of the PV power forecast, an nonparametric kernel density estimation (NKDE) is employed to fit the parameters of the probability density function (PDF) based on the typical climate categories
Summary
With the progressively prominent energy shortage and the deteriorating climate, the significance of renewable energy has reached new heights. Zhu et al [10] provided a distance analysis measure to analyze the correlations between the weather variables and the PV output and used SOM clustering to identify different sample types to develop a PV forecast model rooted in climate clustering recognition. Liu et al [15] applied error calibration to Chinese medium- and long-term power load forecasting by establishing a forecast model that combined general autoregressive conditional heteroscedasticity with regression. To improve the accuracy of wind speed forecasting, a method based on a relevant vector machine and an auto-regressive moving average error correcting is proposed in [17]. Due to the effect of meteorological factors on PV power, a short-term PV power forecast model based on error calibration under typical climate categories is developed, applied to the day-head PV power forecast. The superposition of the forecasted PV power values and the fitted values of the error is conducted to improve the forecast accuracy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
