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

Abstract

With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economically for planning and installation of energy systems like microgrids, etc. The method of solar radiation forecasting and DR influenced energy estimation is compared with the traditional methods to show the efficiency of the proposed method.

Two-Stage Attention Over LSTM With Bayesian Optimization for Day-Ahead Solar Power Forecasting

“…Encoder-decoder networks based on LSTM are becoming popular deep-learning models in time-series forecasting, specifically in sequence-to-sequence mappings [34]- [38]. Therefore, these combinations of encoder-decoder with LSTM can be regarded as state-of-the-art.…”