Short Term Solar Irradiation Prediction Framework Based on EEMD-GA-LSTM Method

  • Anuj Gupta Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India
  • Kapil Gupta Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India
  • Sumit Saroha Guru Jambheshwar University of science and Technology, Hisar, India
Keywords: Solar irradiation, EEMD, genetic algorithm, LSTM, evaluation metrics

Abstract

Accurate short term solar irradiation forecasting is necessary for smart grid stability and to manage bilateral contract negotiations between suppliers and customers. Traditional machine learning methods are unable to acquire and rectify nonlinear characteristics from solar dataset, which not only complicates model construction but also affect prediction accuracy. To address these issues, a deep learning based architecture with predictive analysis strategy is developed in this manuscript. In the first stage, the original solar irradiation sequences are divided into many intrinsic mode functions to generate a prospective feature set using a sophisticated signal decomposition technique. After that, an iteration method is used to generate a prospective range of frequency related to deep learning model. This method is created by linked algorithm using the GA and deep learning network. The findings by the proposed model employing sequences obtained by the preprocessing methodology considerable improve prediction accuracy as comparison to conventional models. In contrast, when confronted with a high resolution dataset derived from big data set, the chosen dataset may not only conduct a huge data reduction, but also enhances forecasting accuracy up to 22.74 percent over a variety of evaluation metrics. As a result, the proposed method might be used to predict short-term solar irradiation with greater accuracy using a solar dataset.

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Author Biographies

Anuj Gupta, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India

Anuj Gupta received the B.Tech in Electronics and Communication Engineering from Kurukshetra University, M.Tech in Electronics and Communication Engineering from Kurukshetra University, Kurukshetra. Presently he is Assistant Professor in EEE Department at Asia Pacific Institute of Information Technology, Panipat and pursuing Ph.D. in the area of solar irradiance forecasting from Electronics and Communication Engineering Department, Maharishi Markandeshwar University, Mullana-Ambala, India. His research area is deregulated electricity market, solar irradiance forecasting. He has more than seven years teaching and research experience.

Kapil Gupta, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India

Kapil Gupta received his B.E. (HONS) degree in Electronics & Communication engineering in 2003 from Rajasthan University and M.E. (HONS) degree in Digital Communication from MBM Engineering College Jodhpur, Rajasthan in 2008. He earned Ph.D. degree in 2013 from MITS University, Rajasthan. Presently he is Associate Professor in the Department of Electronics and Communication Engineering, M.M.E.C, Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala and has more than 15 years of experience in teaching. His research interests are in solar irradiance forecasting, Wireless Sensor Networks, Wireless Communication, Diversity Techniques and Error Correction Coding.

Sumit Saroha, Guru Jambheshwar University of science and Technology, Hisar, India

Sumit Saroha is currently working as Assistant Professor in the Depart- ment of Electrical Engineering, Guru Jambheshwar University of Science & Technology, Hisar, India. He received Ph.D. in the area of forecasting issues in present day power systems. His research interests are transformer design, electricity markets, electricity forecasting, neural networks, wavelet transform, fractional order systems and Multi agent system.

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Published
2022-05-07
Section
Articles