A data-driven approach to optimize the total energy consumption of the HVAC (heating, ventilation, and air conditioning) system in a typical office facility is presented. A multi-layer perceptron ensemble is selected to build the total energy model integrating three indoor air quality models,...

An anticipatory control scheme for optimizing power and vibration of wind turbines is introduced. Two models optimizing the power generation and mitigating vibration of a wind turbine are developed using data collected from a large wind farm. To model the wind turbine vibration, two parameters,...

Recent developments in wind energy research including wind speed prediction, wind turbine control, operations of hybrid power systems, as well as condition monitoring and fault detection are surveyed. Approaches based on statistics, physics, and data mining for wind speed prediction at different...

We present a model for scheduling power generation at a wind farm, and introduce a particle swarm optimization algorithm with a small world network structure to solve the model. The solution generated by the algorithm defines the operational status of wind turbines for a scheduling horizon...

Predicting building energy load is important in energy management. This load is often the result of steam heating and cooling of buildings. In this paper, a data-driven approach for the development of a daily steam load model is presented. Data-mining algorithms are used to select significant...

Daily solar power prediction using data-driven approaches is studied. Four famous data-driven approaches, the Artificial Neural Network (ANN), the Support Vector Machine (SVM), the k-nearest neighbor (kNN), and the multivariate linear regression (MLR), are applied to develop the prediction...

A Markov-switching model in wind speed forecasting is examined in this research. The proposed method employs a regime switching process governed by a discrete-state Markov chain to model the nonlinear evolvement of the wind speed time-series. A Bayesian inference rather than the traditional...