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.
Highlights
Forecasting correct power energy over a span of time is essential for the growth of, and getting benefits from, photovoltaic (PV) technology
The proposed AE-long short-term memory (LSTM) deep learning model was compared with the state-of-the-art deep learning models, i.e., LSTM and gated recurrent unit (GRU), and machine learning model, i.e., random forest regression (RFR)
LSTM and GRU models consist of 4 hidden layers with 32 units in each layer [17]
Summary
Forecasting correct power energy over a span of time is essential for the growth of, and getting benefits from, photovoltaic (PV) technology. Most important factors are the efficiency with which sunlight is converted into power and how this relationship changes with passing time. Is a useful parameter to understand the power decline over a span of time, which plays a key role for PV technology beneficiaries like power companies, businesses and researchers, etc. DR is important from the economical point of view, as higher DR results in decreased power generation over time, decreasing future cash flow [1]. Inaccurate calculation of degradation rates results in increased financial risk [2]. Degradation mechanisms are important from a technical point of view, as they lead to failure of the system [3]. Finding out the science of degradation through modeling and experiments will lead to lifetime improvements
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