Solar Irradiance Forecasting Using Hybrid Ensemble Machine Learning Technique

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Economic development is impacted significantly by conventional energy sources, which are hazardous to humans and the environment. To meet the energy demand and reduce greenhouse gas emissions, the world is shifting towards alternate renewable energy sources. Photovoltaics (PV) is the most common distributed energy source for microgrid formation and one of the world's top renewable energy sources because of their modular design, minimal operational noise, and ease of maintenance. Solar photovoltaic systems, which are photovoltaic panels that turn sunlight into electricity, are one of the most common renewable energy sources. PV production is strongly dependent on solar irradiation, temperature, and other weather conditions. Predicting solar irradiance implies predicting solar power generation one or more steps ahead of time. Prediction increases photovoltaic system development and operation while providing numerous economic benefits to energy suppliers. There are numerous applications that employ prediction to improve power grid operation and planning, with the appropriate time-resolution of the forecast. Stability and regulation necessitate knowledge of solar irradiation over the following few seconds. Reserve management and load following require knowledge of solar irradiation for the next several minutes or hours. To function properly, scheduling and unit commitment requires knowledge about the next few days of solar irradiation. It is crucial to precisely measure solar irradiation since the major issue with solar energy is that it fluctuates because of its variability. Grid operators can control the demand and supply of power and construct the best solar PV plant with the help of accurate and reliable solar irradiance predictions. Electric utilities must generate enough energy to balance supply and demand. The electric sector has consequently focused on Solar PV forecasting to assist its management system, which is crucial for the growth of additional power generation, such as microgrids. Forecasting solar irradiance has always been important to renewable energy generation since solar energy generation is location and time-specific. When the estimated solar generation is available, the grid will function more consistently in unpredictable situations since solar energy generates some quantity of power every day of the year, even on cloudy days.