Research Interests:
Research Interests: Engineering, Computer Science, Sustainable Building Design, Prediction, Energy Efficiency Buildings, and 11 moreBuilding Energy Simulation, Schedule, Occupancy, Artificial Neural Network, Building Integrated Renewable Energy, Energy Consumption Building, Building Energy Management System, Building Energy Prediction, Building Energy Consumption, Orthogonal Arrays, and arXiv
Research Interests:
Research Interests:
Research Interests:
Building energy consumption prediction has been a major concern in the recent years. The energy consumption in building is essential for building manager to manage the building energy management system (BEMS) and energy operator in making... more
Building energy consumption prediction has been a major concern in the recent years. The energy consumption in building is essential for building manager to manage the building energy management system (BEMS) and energy operator in making energy decision services. This paper proposes a building energy consumption prediction with novel pseudo dynamic transitional methods with occupancy profiles and operational power characteristics of buildings. The case study is applied to heating energy consumption of buildings and results show that pseudo dynamic model have coefficient of correlation of energy consumption of error of 0.02%. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational power level of building energy consumption.
Building energy consumption prediction has been a major concern in the recent years. The energy consumption in building is essential for building manager to manage the building energy management system (BEMS) and energy operator in making... more
Building energy consumption prediction has been a major concern in the recent years. The energy consumption in building is essential for building manager to manage the building energy management system (BEMS) and energy operator in making energy decision services. This paper proposes a building energy consumption prediction with novel pseudo dynamic transitional methods with occupancy profiles and operational power characteristics of buildings. The case study is applied to heating energy consumption of buildings and results show that pseudo dynamic model have coefficient of correlation of energy consumption of error of 0.02%. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational power level of building energy consumption.
Research Interests:
Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in... more
Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines ...