Short term solar insolation prediction: P-ELM approach

• Published in: International journal of parallel, emergent and distributed systems Vol. 33; no. 6; pp. 663 - 674
• Main Authors: Syed, Mahaboob Shareef, Suresh, C. V., Sivanagaraju, S.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.11.2018; Taylor & Francis Ltd
• Subjects: Algorithms; ANFIS: Artificial Neural Fuzzy Inference System; ANN: Artificial neural networks; AR: Autoregressive; ARIMA: Autoregressive integrated moving average; ARMA: Auto regressive moving average; ARX: autoregressive model with exogenous input; CI: Clearness Index; clearness index; Computer simulation; CRO: Coral reefs optimization; DHI: Diffuse horizontal irradiance; DNI: Direct normal irradiance; ELM: Extreme learning machine algorithms; FNN: feed-forward neural network; Forecasting; GA: Genetic algorithm; GHI: Global horizontal irradiance; Irradiance; ISFOC: Spanish Institute for Concentration Photo Voltaic Systems; LLR: Local linear regression; Machine learning; MAE: Mean absolute error; mean absolute error; MLP: Multilayer perceptron algorithm; MLPNN: Multi-layered perceptron neural network; NARX: Nonlinear autoregressive models with exogenous; Neural networks; NN: Neural network; NNE: Neural network ensemble; NRMSE: Normalized root means square error; NSRDB: National solar radiation database; NWP: Numeric weather prediction; P-ELM: Persistent extreme learning machine; persistence-extreme learning machine; PV: Photo voltaic; Rainfall; RBF: Radial basis function; RBFNN: Radial basis function neural network; Real time; RMSE: Root mean square error; RNMSE: Rooted normalized mean square error; RNN: Recurrent neural network; root mean square error; SVD: Singular value decomposition; SVM: Support vector machine; Weather
• ISSN: 1744-5760; 1744-5779
• DOI: 10.1080/17445760.2017.1404601

The accurate forecasting of solar irradiance with hybrid machine learning algorithm is presented in this paper. A novel Persistence-Extreme Learning Machine (P-ELM) algorithm is used for training of the system. The Clearness Index (CI) value of the 22 districts of Andhra Pradesh (India) is calculated and out of which four areas are identified with highest CI values. The Global Horizontal Irradiance (GHI) is forecasted for the selected areas with different weather conditions such as winter, summer and rainfall seasons using a P-ELM algorithm. The input parameters are Temperature, Diffuse horizontal irradiance, pressure and past GHI and GHI for the next instant as the output is considered. The real time data is obtained for every one hour interval for a period of one month. The performance of the P-ELM algorithm is evaluated in terms of Mean Absolute Error and Root Mean Square Error. From the obtained results, it is observed that P-ELM algorithm offers better performance over the fundamental P-ELMs. The P-ELM algorithm gives good forecasting accuracy with minimum simulation time. The simulation of P-ELM algorithm is carried out using MATLAB 2013a environment. The P-ELM algorithm is very much beneficial for accurate and reliable real time solar forecasting. To forecast solar insolation Persistent - Extreme Learning Machine algorithm was used.