With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.
The cloud to ground (CG) lightning has negative impacts on humans and properties. A lightning strike is a great concern to mankind and industry because of its detrimental impact on human safety, hazard, and equipment failures. There are different lightning detection methods, including time difference of arrival (TDOA) and magnetic direction finding (MDF). Using combined techniques is an innovative approach to achieve higher location accuracy and detection efficiency of lightning flashes. In this investigation, a Lightning locating system (LLS) was designed and implemented at University Technology Malaysia (UTM), Johor, Malaysia to detect the cloud to ground lightning discharges in a study area of 400 km2. A particle swarm optimization (PSO) algorithm was applied in this study as the combination mediator to find the optimum point of the lightning strike. The PSO was initialized by 30 particles based on the results of the MDF and TDOA methods. The performance of the PSO-based algorithm is known to be affected by the arrangement of the searching process. The results of the detected lightning strikes by the PSO-based LLS were validated using an industrial lightning detection system for December and March. In addition, the whole study area was divided into 36 equal sections to analyze the abundance of CG discharges in each section. From the experimental data, the mean distance differences between the PSO-based LLS and the industrial LLS inside the study area varied from 0 to 573 m. Therefore, the proposed PSO-based LLS is efficient and accurate to detect and map the lightning discharges occurring within the coverage area. Although an industrial LLS monitors a large area or a country with several sensors, the detected lightning discharges and the statistical data analysis of the captured flashes are not obtainable by public individuals. Meanwhile, estimation and localization of lightning strikes are necessary for the public to mitigate the problems related to lightning discharges. Moreover, this study will be significant for the researchers, the insurance companies, and public users to be aware of the detected storms and estimation of imminent rainfalls. The PSO-based LLS provides an accurate lightning detection system for a specific local region and can be implemented to regional scale in other parts of the world.
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