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

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Vivek Vijay, Rakesh Kumar, Ashish Sharma, Abhishek Kumar

Vivek Vijay, Rakesh Kumar, Ashish Sharma and Abhishek Kumar. Short-Term Forecasting of Solar Irradiance using STL, Wavelet and LSTM. International Journal of Computer Applications 183(46):9-17, January 2022. BibTeX

	author = {Vivek Vijay and Rakesh Kumar and Ashish Sharma and Abhishek Kumar},
	title = {Short-Term Forecasting of Solar Irradiance using STL, Wavelet and LSTM},
	journal = {International Journal of Computer Applications},
	issue_date = {January 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}


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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Forecasting, Ensemble Forecast, renewable energy, wavelet transform