Conventional fossil fuels are depleting daily due to the growing human population. Previous
research has proved that renewable energy sources, especially solar and wind, can be …

The demand for energy generation from solar energy resource has been exponentially
increasing in recent years. It is integral for a grid operator to maintain the balance between …

In this study, three models of machine learning algorithms are implemented to predict wind
speed, wind direction and output power of a wind turbine. The first model is multilayer feed …

Abstract Machine learning algorithms (MLAs) are applied to predict wind speed data for
Osorio wind farm that is located in the south of Brazil, near the Osorio city. Forecasting wind …

Rainfall-runoff models can be classified into three types: physically based models,
conceptual models, and empirical models. In this latter class of models, the catchment is …

This monograph summarizes and analyzes recent research by the authors and others to
understand, characterize, and model solar resource variability. This research shows that …

Prediction of building energy consumption plays an important role in energy conservation,
management, and planning. Continuously improving and enhancing the performance of …

Lately, the kernel extreme learning machine (KELM) has gained considerable importance in
the scientific area due to its great efficiency, easy implementation and fast training speed. In …

This research paper aims to develop a hybrid neuro-evolutionary wrapper-based model for
daily global solar radiation estimation in the solar-rich Sunshine State of Queensland …

Abstract Machine learning (ML) algorithms have been increasingly successful in their
applications to solve energy and environmental engineering problems. ML algorithms have …