计算机科学
人工神经网络
光伏系统
非线性自回归外生模型
集成学习
极限学习机
支持向量机
太阳能
自回归模型
人工智能
决策树
太阳能
机器学习
功率(物理)
工程类
数学
统计
物理
电气工程
量子力学
作者
Aanchit Nayak,Leena Heistrene
出处
期刊:2020 International Conference on Smart Grids and Energy Systems (SGES)
日期:2020-11-01
被引量:7
标识
DOI:10.1109/sges51519.2020.00167
摘要
Solar power generation through photovoltaic technology is one of the most popular renewable energy sources. But solar energy is a non-dispatchable source and it is dynamic in nature. Hence from the power system operation point of view, solar power forecasting becomes imperative for a stable grid operation. In this paper, a novel hybrid machine learning approach is proposed for forecasting solar power generation through a hybrid Ensemble Averager technique which exploits the advantages of different machine learning approaches and incorporates them into a single model. Missing values in insolation have been dealt with using a univariate regression-based imputation technique. The ensemble averager is a weighted average model of five individual models, namely - a non-linear autoregressive neural network (NAR-NN), a non-linear autoregressive neural network with exogenous signal (NARX-NN), a least square boosted decision tree model, a support vector regressor with RBF kernel and an Extreme Learning Machine (ELM). The proposed model is tested on a real-world dataset of a 1 MW solar park situated in Gujarat, India (23°09'15.1”N 72°40'00.8”E). Proposed model shows better performance as compared to other models.
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