Bayesian Optimization - LSTM Modeling and Time Frequency Correlation Mapping Based Probabilistic Forecasting of Ultra-short-term Photovoltaic Power Outputs
Jie Shi, Yuming Wang, Yue Zhou, Yan Ma, Jie Gao, Shude Wang, Zuan Fu
Abstract
Due to the fluctuation and randomness of photovoltaic power over time, accurate and reliable ultra-short-term photovoltaic power forecasting is significant for real-time dispatch and frequency regulation of power grids. In this article, the improved BO-LSTM forecasting frame considering frequency correlation mapping is proposed. Firstly, the features of photovoltaic power are extracted and resolved according to power series frequency segments. Then, the established BO-LSTM forecasting model is adjusted based on the above extracted features in separate segment, and the results of deterministic forecasting are obtained. Furthermore, in order to obtain the reliable performance, the time-correlation algorithm is employed into the above deterministic forecasting model, which offers the base for probabilistic power forecasting. Finally, the above algorithms and forecasting framework are applied to the measurement data from a commercial photovoltaic power station in North China. Compared to the benchmark models, the Power Interval Normalized Average Width (PINAW) error of the proposed ultra-short-term forecasting algorithm has shown satisfied improvements. The PINAW has reduced by 8.4% (v.s. Adam-LSTM), 48.9% (v.s. Sgd-LSTM), 52.8% (v.s. Adagrad-LSTM), 9.1% (v.s. Rmsprop-LSTM), 97.2% (v.s. Adadelta-LSTM), 86.8% (v.s. Adam-mlp), 87.4% (v.s. Sgd-mlp), 90.9% (v.s. Adagrad-mlp), 86.5% (v.s. Rmsprop-mlp), and 99.7% (v.s. Adadelta-mlp).