Day-ahead photovoltaic power non-parametric probability prediction method based on QRA-LSTM

Abstract

The invention discloses a day-ahead photovoltaic power non-parametric probability prediction method based on QRA-LSTM. Photovoltaic historical data and numerical weather forecast data (NSW) are adopted to train a group of mutually independent long-term and short-term memory (LSTM) deterministic prediction models, and a quantile regression average algorithm (QRA) is adopted to integrate each independent LSTM prediction model to generate a non-parametric probability prediction model of photovoltaic output. Non-parametric probability prediction can describe the uncertainty problem which is difficult to reflect by simple deterministic prediction, and the result has higher credibility. The method can effectively avoid the problem that deterministic prediction and probability prediction are separately considered, provides an important basis for decision scheduling of scheduling personnel, and is huge in application value and prospect.

Description

technical field [0001] The invention belongs to the technical field of photovoltaics, and in particular relates to a method for non-parametric probability prediction of day-ahead photovoltaic power. Background technique [0002] In recent years, new energy power generation such as photovoltaics and wind power has developed rapidly, and the installed capacity of photovoltaics worldwide has increased year by year. However, as the penetration rate of photovoltaics in the grid gradually increases, the inherent uncertainty and volatility of photovoltaic power generation poses challenges to the safe operation of the grid and power quality. In order to consume the grid-connected power of photovoltaics as much as possible, dispatchers need to fully understand the output characteristics of photovoltaics. The short-term day-ahead photovoltaic output forecast can give the predicted value of photovoltaic output in the next day, which provides an important basis for dispatchers' decisio...

AI Technical Summary

Problems solved by technology

However, the existing methods mainly start from the probabilistic forecasting model, and treat deterministic forecasting and probabilistic forecasting separately.

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