Solar energy predictionMachine learningPhotovoltaicSOFT-computingANNSVMAt present, generating energy from renewable sources is an important topic and is attracting significant attention because of its many benefits. Recent technological developments have made generating renewable energy from various sources such...
Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets,...
Load Hindcasting: A Retrospective Regional Load Prediction Method Using Reanalysis Weather Data To avoid a tendency for overfitting, the GA-based method employs triple cross-validation as a fitness function. Results indicate a regional mean absolute percent error (MAPE) of less than 3% over all hou...
By providing EEG-related data and prediction models at different spatial scales, the CEEG dataset will facilitate the advancement of research on household energy consumption, HA consumer choice, and the assessment of EEL-related policies. Methods A schematic of the process of generating the CEEG data...
The Prediction and Control of Self-Excited Oscillations Due to Series Capacitors in Power Systems This paper covers the analysis of the problem of self-excited current oscillations which may occur when synchronous machines are closely coupled to transmi... LA Kilgore,LC Elliott,ER Taylor - 《IEEE...
Different approaches such as distributed architectures, artificial intelligence, edge AI, data mining, and user-server focused architectures are commonly used in the energy sector today. These approaches offer effective solutions in important areas such as energy production, data analysis and prediction, ...
These methods are employed in wind energy, grid management, solar irradiance prediction for photovoltaic systems, electric load forecasting, and power generation and consumption. Due to the fact that, the methods and algorithms are unable to give satisfactory results with high precision, therefore, ...
To bridge this research gap, we propose MASSTAR: a Multi-modal lArge-scale Scene dataset with a verSatile Toolchain for surfAce pRediction and completion. We develop a versatile and efficient toolchain for processing the raw 3D data from the environments. It screens out a set of fine-grained ...
Influenced by the southeastern monsoon, China has abundant precipitation in the summer, and the prediction error is large. For other variables, the seasonal characteristics of SSD model accuracy were consistent with those of PRE. In contrast, RHU and PRS were high in summer and low in winter. ...
The calculation of UTCI requires four input parameters: air temperature (Ta), mean radiant temperature (MRT), wind speed, and humidity24. The solar and thermal radiation fluxes (ssrd,ssr,fdir,strdandstr) are the variables necessary for the calculation of the MRT32which is used to assess the...