Clean electricity system based on renewable generation, such as wind power, hydropower, and solar power, is rapidly growing around the world. Based on a report by International Energy Agency (IEA) [1], cumulative solar Photovoltaic (PV) capacity alone reached almost 400 Gigawatt (GW) and generated over 460 Terawat-hour (TWh) in 2017. This represents around 2% of global power output. By the year 2023, the world will have 1 trillion watts of installed solar PV capacity. Solar power penetration is growing and measures up commendably with the major renewable countries, where the electricity generated from PV system was 5.9% of the national electricity production for Australia, 6.5% in Japan and 7.1% in Italy in 2018 [2], and 8.2% in Germany in 2019 [3]. Despite the rapid increase in solar power penetration, there is still substantial room for growth, considering these countries are advanced economies with high energy consumption needs. To promote sustainability while meeting future energy needs, renewable generation such as solar-generated electricity as a solution is not always straightforward. One issue of concern is the intermittency of solar power. The intermittency can be due to the “variability” of solar power generation due to fluctuations in solar radiation caused by frequent changing of cloud conditions during the day, and “uncertainty” due to the electricity generation that is not known with perfect accuracy at multiple timescales, from seconds to minutes to hours. The intermittency issue seriously impacts the system operator's ability to manage the supply and demand in the electricity grid. Therefore, it would be of special interest to investigate ways to predict the electricity generation of the solar PV systems more accurately. This work aims to develop more sophisticated methods of predictions and demonstrate the feasibility of using machine learning techniques to solve energy‐related problems.    References: [1] https://www.iea.org/topics/renewables/solar/ [2] https://ec.europa.eu/jrc/sites/jrcsh/files/kjna29938enn_1.pdf [3] https://www.ise.fraunhofer.de/content/dam/ise/en/documents/publications/studies/recent-facts-about-photovoltaics-in-germany.pdf   Team Name R.Energetics (means data analytics in renewable energy) Track Energy Use Case Prediction of solar irradiance for solar PV system Team Lead Yong Wee Foo @YWF  Member Ese Omats @EseOmats1425      Dataset 2013 to 2016, 2018 to 2020 Predictor variables Ambient Temperature Relative Humidity Rain Gauge measurement Wind Speed Wind Direction Atmospheric Pressure PV panel surface Temperature Air quality data Response variable Solar irradiance Modeling Technique Artificial Neural Networks, Forest, Gradient Boosting, Ensemble Short Video Final video for submission Fresh video ... View more