Analysis on intelligent machine learning enabled with meta-heuristic algorithms for solar irradiance prediction
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RESEARCH PAPER
Analysis on intelligent machine learning enabled with meta‑heuristic algorithms for solar irradiance prediction T. Vaisakh1 · R. Jayabarathi1 Received: 12 February 2020 / Revised: 8 September 2020 / Accepted: 27 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The solar forecasting is an effective method to enhance the operation of an electrical system for merging a large amount of solar power generation systems and intends to expand a new empirical method to model the prediction uncertainty of the solar irradiance. The proposed model comprises three phases, such as (a) Data Acquisition, (b) Feature Extraction, and (c) Prediction. Initially, benchmark data available from local meteorological organizations are collected that includes the numerical weather forecasting data like temperature, dew point, humidity, visibility, wind speed, and other descriptive information. Once the data is collected, feature extraction is done by first-order and second-order statistical models. First Order Statistics, like mean, median, standard deviation, the maximum value of entire data, and minimum value of entire data, and Second-Order Statistics, like Kurtosis, skewness, correlation, and entropy are extracted as the features. These features are further applied to three machine learning algorithms named Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). As main novelty of this paper, the number of hidden neurons of all these networks is optimized by a hybrid algorithm merging both the Deer Hunting Optimization Algorithm (DHOA) and Grey Wolf Optimization (GWO), which is named as Grey Updated DHOA (GU-DHOA). The improvement of these networks with the assistance of a hybrid meta-heuristic algorithm will be highly effective for solar irradiance prediction, overcoming the existing machine learning algorithms. Keywords Power distribution system · Solar panel · Solar irradiance prediction · Statistical features · Optimized machine learning algorithms · Hybrid optimization Abbreviations BIPV Building Integrated Photo Voltaics STC Solar Thermal Collectors GWO Grey Wolf Optimization DHOA Deer Hunting Optimization Algorithm PV Photo Voltaics GHI Global Horizontal Irradiance DNI Direct Normal Irradiance ARIMA Autoregressive Integrated Moving Average ARMAX Autoregressive Moving Average with Exogenous Inputs ARMA Autoregressive Moving Average SVM Support Vector Machine ANN Artificial Neural Networks LSTM Long Short-Term Memory * T. Vaisakh [email protected] 1
Electrical and Electronics Engineering, Amrita School of Engineering, Coimbatore, India
BPNN Back Propagation Neural Network RMSE Root Mean Square Error PDF Probability Distribution Function NWP Numerical Weather Prediction ESS Energy Storage System rRMSE relative RMSE MLP MultiLayer Perceptron GU-DHOA Grey Updated DHOA CNN Convolutional Neural Network MEP Mean Error Percentage RNN Recurrent Neural Network DNN Deep Neural Network MASE Mean Absolute Scaled Er
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