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...

A data-driven optimization approach for minimization of the cooling output of an air handling unit (AHU) is presented. The models used in this research are built with data mining algorithms. The performance of dynamic models build by four different data mining algorithms is studied. A model...

A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and...

A data-driven approach for modeling indoor-air-quality (IAQ) sensors used in heating, ventilation, and air conditioning (HVAC) systems is presented. The IAQ sensors considered in the paper measure three basic parameters, temperature, CO2, and relative humidity. Three models predicting values of...