光伏系统
水准点(测量)
概率预测
概率逻辑
计算机科学
随机性
期限(时间)
电力系统
算法
人工智能
功率(物理)
机器学习
数学
统计
工程类
物理
大地测量学
量子力学
电气工程
地理
作者
Jie Shi,Yuming Wang,Yue Zhou,Yan Ma,Jie Gao,Shude Wang,Zuan Fu
出处
期刊:IEEE Transactions on Industry Applications
[Institute of Electrical and Electronics Engineers]
日期:2024-03-01
卷期号:60 (2): 2422-2430
标识
DOI:10.1109/tia.2023.3334700
摘要
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 paper, 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).
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