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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,...

A data-driven approach for optimizing the reheat process in a variable-air-volume box is presented. Data-mining algorithms derive temporal predictive models from the reheat process data. The bi-objective model formed is solved with a modified particle swarm optimization algorithm. To increase...

In this paper, a two-mode ventilation control of a single facility is formulated as a scheduling model over multiple time horizons. Using the CO2 concentration as the major indoor air quality index and expected room occupancy schedule, optimal solutions leading to reduced CO2 concentration and...

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...

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...

This paper addresses the issue of forecasting the term structure. We provide a unified state-space modelling framework that encompasses different existing discrete-time yield curve models. Within such framework we analyze the impact on forecasting performance of two crucial modelling choices,...

We use time-varying regression to model the relationship between returns in the Shanghai and New York stock markets, with possible inclusion of lagged returns. The parameters of the regressions reveal that the effect of current stock return of New York on Shanghai steadily increases after the...