论文标题
高效的机器学习方法，用于准确的短期太阳能预测
An Efficient Machine Learning Approach for Accurate Short Term Solar Power Prediction
论文作者
论文摘要
近年来，基于太阳能的电力世代经历了强劲而有影响力的增长。间歇性太阳能的法规，调度，调度和单位承诺取决于预测方法的准确性。在本文中，提出了强大的扩展极限学习机（EELM），以准确预测不同时间范围和天气条件的太阳能。所提出的EELM技术由于缺乏随机输入层的重量而没有随机性，并且需要较少的时间来有效预测太阳能。通过各种绩效指标从国家可再生能源实验室（NREL）收集的历史数据来验证拟议的EELM的性能。提出的EELM方法的功效针对基本的ELM和功能连接神经网络（FLNN）进行了5分钟和1小时的提前时间。
Solar based electricity generations have experienced strong and impactful growth in recent years. The regulation, scheduling, dispatching, and unit commitment of intermittent solar power is dependent on the accuracy of the forecasting methods. In this paper, a robust Expanded Extreme Learning Machine (EELM) is proposed to accurately predict solar power for different time horizons and weather conditions. The proposed EELM technique has no randomness due to the absence of random input layer weights and takes very less time to predict the solar power efficiently. The performance of the proposed EELM is validated through historical data collected from the National Renewable Energy Laboratory (NREL) through various performance metrics. The efficacy of the proposed EELM method is evaluated against basic ELM and Functional Link Neural Network (FLNN) for 5 minutes and 1 hour ahead time horizon.
