A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model

Sourav Malakar,Bhaswati Ganguli,K Boopathi,A G Rangaraj,Amlan Chakrabarti,Saptarsi Goswami,Sugata Sen Roy

doi:10.3390/make3040047

Abstract

Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of deep-learning architectures, among which Bidirectional Gated Recurrent Unit (BGRU) has apparently not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India.

Highlights

IntroductionIndia is ranked third after China and the United States of America (USA) in terms of solar energy development [2]
Solar energy is one of the important components of the alternative sources of energy [1].India is ranked third after China and the United States of America (USA) in terms of solar energy development [2]
We found that statistical and Artificial Neural Network (ANN)-based models were effective in intra-hour or intra-day

Summary

Introduction

India is ranked third after China and the United States of America (USA) in terms of solar energy development [2]. The. Global Horizontal Irradiance (GHI) is often taken as a proxy for solar energy generation and used for the prediction task [5,6,7,8,9,10,11,12]. In [10], the authors categorized solar forecasting horizons into short-term, mediumterm, and long-term forecasting. For one to few hours ahead of solar forecasting, i.e., for short-term forecasting, currently, machine-learning models are the state-of-the-art models [14]. In [16], the authors stated that short-term solar forecasting is essential for balancing demand and supply and decreasing the storage requirement, unit commitment, etc. We found that statistical and Artificial Neural Network (ANN)-based models were effective in intra-hour (short-term) or intra-day (medium-term) solar forecasting

Methods

Results

Conclusion