A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

Abstract

Precise solar radiation forecasting is of great importance for solar energy utilization and its integration into the grid, but because of the daily solar radiation’s intrinsic non-stationary and nonlinearity, which is influenced by a lot of elements, single predicting models may have difficulty obtaining results with high accuracy. Therefore, this paper innovatively puts forward an original hybrid model that predicts solar radiation through extreme learning machine (ELM) optimized by the bat algorithm (BA) based on wavelet transform (WT) and principal component analysis (PCA). First, choose the meteorological variables on the basis of Pearson coefficient test, and WT will decompose historical solar radiation into two time series, which are de-noised signal and noise signal. In the approximate series, the lag phase of historical radiation is obtained by partial autocorrelation function (PACF). After that, use PCA to reduce the dimensions of the influencing factors, including meteorological variables and historical radiation. Finally, ELM is established to predict daily solar radiation, whose input weight and deviation thresholds gained optimization by BA, thus it is called BA-ELM henceforth. In view of the four distinct solar radiation series obtained by NASA, the empirical simulation explained the hybrid model’s validity and effectiveness compared to other primary methods.

Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis

“…PCA has been successfully used for dimensionality reduction of different models in for many applications such as biochemical oxygen demand (BOD) and solar radiation forecasting, etc. [20,21].…”

An optimise ELM by league championship algorithm based on food images

“…Various optimisationbased ELM, in addition to SA-ELM, were found in the literature: the input weights and bias of ELM have been optimised by Sun [10], based on Particle Swarm Optimisation (PSO). Bat algorithm are used by both [11], for optimisation of the biases and weights of ELM. Competitive swarm optimizer has been applied by [12], to optimise the standards of the hidden neurons and the input weights of the Extreme Learning Machine.…”

AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System

“…Specifically, extensions of recurrent neural network, i.e., long short-term memory unit (LSTM) and gated recurrent unit (GRU) are more suitable for time series problems, due to their inherent characteristics of learning long-term dependencies [15,16]. Mostly, deep learning model-based forecasting is short-term forecasting ranging from a few hours to days [17][18][19]. Multi-time steps ahead solar irradiance forecasting of up to 120 minutes is performed in [20].…”