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22 July 2024
Reseach Article

Short-Term Forecasting of Solar Irradiance using STL, Wavelet and LSTM

by Vivek Vijay, Rakesh Kumar, Ashish Sharma, Abhishek Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 46
Year of Publication: 2022
Authors: Vivek Vijay, Rakesh Kumar, Ashish Sharma, Abhishek Kumar

Vivek Vijay, Rakesh Kumar, Ashish Sharma, Abhishek Kumar . Short-Term Forecasting of Solar Irradiance using STL, Wavelet and LSTM. International Journal of Computer Applications. 183, 46 ( Jan 2022), 9-17. DOI=10.5120/ijca2022921829

@article{ 10.5120/ijca2022921829,
author = { Vivek Vijay, Rakesh Kumar, Ashish Sharma, Abhishek Kumar },
title = { Short-Term Forecasting of Solar Irradiance using STL, Wavelet and LSTM },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2022 },
volume = { 183 },
number = { 46 },
month = { Jan },
year = { 2022 },
issn = { 0975-8887 },
pages = { 9-17 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2022921829 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:20:06.530517+05:30
%A Vivek Vijay
%A Rakesh Kumar
%A Ashish Sharma
%A Abhishek Kumar
%T Short-Term Forecasting of Solar Irradiance using STL, Wavelet and LSTM
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 46
%P 9-17
%D 2022
%I Foundation of Computer Science (FCS), NY, USA

We propose a hybrid framework for short-term forecasting based on decomposition techniques and LSTM (Large Short Term Memory) algorithm. The study aims to analyze and quantify solar irradiance forecast using two types of decomposition techniques: decomposition of time series into the time domain and frequency domain via locally weighted regression based on Loess (STL) and using discrete wavelet transformation (DWT) respectively. LSTM is used to forecast the decomposed detail and approximation components. The final forecast of GHI (Global Horizontal Irradiance) is then obtained by combining these components using inverse wavelet transform. Hourly data from the Indian Meteorological Department (IMD) Jodhpur Rajasthan from January 2017 to June 2019 is used to demonstrate the proposed algorithm. The forecast accuracy of the proposed model is compared with other competitive models. The observed results show that the proposed combination of STL, wavelet transform, and LSTM outperforms (1) LSTM, (2) combination of STL and LSTM, (3) Bi-LSTM and (4) the persistence model.

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Index Terms

Computer Science
Information Sciences


Forecasting Ensemble Forecast renewable energy wavelet transform