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.
Highlights
Traditional electric service mechanisms are undergoing rapid and continuous changes with the increasing penetration of economical, reliable, and environmentally friendly microgrids [1,2].A microgrid consists of distributed generation devices, such as wind turbines (WTs) and photovoltaics (PVs), an energy storage system (ESS), and controllable loads
The models compared were the state of the art models: support vector regression (SVR), recurrent neural network (RNN), feed forward neural network (FFNN), long short term memory (LSTM), and gated recurrent units (GRUs)
Solar power generation forecasting mainly depends on the amount of solar radiation
Summary
Traditional electric service mechanisms are undergoing rapid and continuous changes with the increasing penetration of economical, reliable, and environmentally friendly microgrids [1,2]. A microgrid consists of distributed generation devices, such as wind turbines (WTs) and photovoltaics (PVs), an energy storage system (ESS), and controllable loads. It can efficiently manage generation and loads and operate in the grid-connected and islanding mode, enabling it to exchange energy between a main-grid and neighboring microgrids. Different regions around the world are investing into microgrids, expecting huge increments in revenue.
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