Day Ahead Solar Irradiation Forecasting Based on Extreme Learning Machine

A. Rehiara, Sabar Setiawidayat
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Abstract

Solar radiation data is very important for humans in meteorology, agriculture and energy. An Extreme Learning Machine (ELM) model is a data-based model developed from a single hidden layer feed-forward neural network (SLFN) which has the superiority in terms of training speed that is better than its predecessor generation. A model for predicting solar radiation in the Manokwari area and its surroundings was built with the ELM algorithm. The model has been used to predict daily solar radiation in the area. The ELM model has been trained using 8016 data solar irradiation and temperature from NASA. The test results show that the built has fairly high accuracy with MAE values of about 0.6392 in a training time of 4.4375 seconds. The ELM model has superiority in time consuming compared to a simple feedforward neural network.
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基于极限学习机的日前太阳辐射预报
太阳辐射数据在气象、农业和能源等领域对人类非常重要。极限学习机(Extreme Learning Machine, ELM)模型是在单隐层前馈神经网络(SLFN)基础上发展起来的基于数据的模型,在训练速度上优于上一代模型。利用ELM算法建立了马诺瓦里地区及其周边地区太阳辐射的预测模型。该模式已被用来预测该地区的日太阳辐射。ELM模型使用NASA 8016太阳辐射和温度数据进行了训练。测试结果表明,在4.4375秒的训练时间内,构建的模型具有较高的准确率,MAE值约为0.6392。与简单的前馈神经网络相比,ELM模型在时间消耗方面具有优势。
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