Abstract
More accurate self-forecasting not only provides a better-integrated solution for electricity grids but also reduces the cost of operation of the entire power system. To predict solar photovoltaic (PV) power generation (SPVG) for a specific hour, this paper proposes the combination of a two-step neural network bi directional long short-term memory (BD-LSTM) model with an artificial neural network (ANN) model using exponential moving average (EMA) preprocessing. In this study, four types of historical input data are used: hourly PV generation for one week (168 h) ahead, hourly horizontal radiation, hourly ambient temperature, and hourly device (surface) temperature, downloaded from the Korea Open Data Portal. The first strategy is employed using the LSTM prediction model, which forecasts the SPVG of the desired time through the data from the previous week, which is preprocessed to smooth the dynamic SPVG using the EMA approach. The SPVG was predicted using the LSTM model according to the trend of the previous time-series data. However, slight errors still occur because the weather condition of the time is not reflected at the desired time. Therefore, we proposed a second strategy of an ANN model for more accurate estimation to compensate for this slight error using the four inputs predicted by the LSTM model. As a result, the LSTM prediction model with the ANN estimation model using EMA preprocessing exhibited higher accuracy in performance than other options for SPVG.
Highlights
A report from the International Energy Agency (IEA) revealed that solar, wind, and hydropower energy is growing at a fast rate
Conclusions estimating the artificial neural network (ANN) of four types of input data at the designated time based on long short-term memory (LSTM) prediction
This paper proposed a new approach (ELA) to improve the accuracy of a two-step model by estimating the ANN of four types of input data at the designated time based on LSTM prediction modeling with exponential moving average (EMA) smoothing preprocessing
Summary
A report from the International Energy Agency (IEA) revealed that solar, wind, and hydropower energy is growing at a fast rate. In the Renewables 2018 forecast, the market analysis and forecasts from 2019 to 2024 by the IEA [1], the segment of renewables satisfying global energy demand is expected to grow by one-fifth in the following five years to reach 12.4% in 2023. South Korea is one of the most developed countries in Asia, and it is the eighth largest electricity consumer in the world [2]. South Korea has been making a great effort to increase the renewable energy portion of their energy mix [3]. The country has a strong solar photovoltaic (PV) manufacturing industry and supportive policies to achieve the national renewable energy target of 20% by 2030.
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