Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study

Abstract

Microgrid is becoming an essential part of the power grid regarding reliability, economy, and environment. Renewable energies are main sources of energy in microgrids. Long-term solar generation forecasting is an important issue in microgrid planning and design from an engineering point of view. Solar generation forecasting mainly depends on solar radiation forecasting. Long-term solar radiation forecasting can also be used for estimating the degradation-rate-influenced energy potentials of photovoltaic (PV) panel. In this paper, a comparative study of different deep learning approaches is carried out for forecasting one year ahead hourly and daily solar radiation. In the proposed method, state of the art deep learning and machine learning architectures like gated recurrent units (GRUs), long short term memory (LSTM), recurrent neural network (RNN), feed forward neural network (FFNN), and support vector regression (SVR) models are compared. The proposed method uses historical solar radiation data and clear sky global horizontal irradiance (GHI). Even though all the models performed well, GRU performed relatively better compared to the other models. The proposed models are also compared with traditional state of the art methods for long-term solar radiation forecasting, i.e., random forest regression (RFR). The proposed models outperformed the traditional method, hence proving their efficiency.

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

“…The proposed AE-LSTM deep learning model was compared with the state-of-the-art deep learning models, i.e., LSTM and GRU, and machine learning model, i.e., RFR. LSTM and GRU models consist of 4 hidden layers with 32 units in each layer [17].…”

“…Each LSTM unit consists of an internal memory with three gates, i.e., input gate, output gate and forget gate as shown in Figure 10. The input gate decides how much current information needs to be passed, the forget gate decides the information needing to be forgotten from the previous state, while the output gate decides the internal state information that needs to be passed [17]. The internal memory, also called the cell state, remembers important information.…”

“…The internal memory, also called the cell state, remembers important information. Equations for LSTM gates and internal memory are given in [17]. With these gates and memory, LSTM can learn important initial information and carry it over long-distance, hence, capturing long-term dependencies.…”

“…Specifically, extensions of recurrent neural network, i.e., long short-term memory unit (LSTM) and gated recurrent unit (GRU) are more suitable for time series problems, due to their inherent characteristics of learning long-term dependencies [15,16]. Mostly, deep learning model-based forecasting is short-term forecasting ranging from a few hours to days [17][18][19]. Multi-time steps ahead solar irradiance forecasting of up to 120 minutes is performed in [20].…”

“…However, DR influenced energy estimation requires solar radiation data over a longer span of time. Year ahead solar radiation was forecasted using simple LSTM and GRU models [17]. However, these methods were trained in a supervised manner without optimal feature extraction and did not estimate the DR influenced energy of PV systems.…”