Today, buildings consume more than 40% of primary energy in and produce more than 36% of CO₂. So, an intelligent controller applied to the buildings for energy and comfort management could achieve significant reduction in energy consumption while improving occupant’s comfort. Conventional on/off controllers were only able to automate the tasks in building and were not well suited for energy optimization tasks. Therefore, building energy management has become a focal point in recent years, promising the development of various technologies for various scenarios. This paper deals with a state of the art review on recent developments in building energy management system (BEMS) and occupants comfort, focusing on three model types: white box, black box, and gray box models. Through a comparative study, this paper presents pros and cons of each model.

Over recent years, the independent adoption of lean construction and building information modeling (BIM) has shown improvements in construction industry efficiency. Because these approaches have overlapping concepts, it is thought that their synergistic adoption can bring many more benefits. Today, implementing the lean–BIM theoretical framework is still challenging for many companies. This paper conducts a comprehensive review with the intent to identify prevailing interconnected lean and BIM areas. To this end, 77 papers published in AEC journals and conferences over the last decade were reviewed. The proposed weighting matrix showed the most promising interactions, namely those related to 4D BIM-based visualization of construction schedules produced and updated by last planners. The authors also show evidence of the lack of a sufficiently integrated BIM–Last Planner System^® framework and technologies. Thus, we propose a new theoretical framework considering all BIM and LPS interactions. In our model, we suggest automating the generation of phase schedule using joint BIM data and a work breakdown structure database. Thereafter, the lookahead planning and weekly work plan is supported by a field application that must be able to exchange data with the enterprise resource planning system, document management systems, and report progress to the BIM model.

This paper proposes an approach to develop building dynamic thermal models that are of paramount importance for controller application. In this context, controller requires a low-order, computationally efficient, and accurate models to achieve higher performance. An efficient building model is developed by having proper structural knowledge of low-order model and identifying its parameter values. Simplified low-order systems can be developed using thermal network models using thermal resistances and capacitances. In order to determine the low-order model parameter values, a specific approach is proposed using a stochastic particle swarm optimization. This method provides a significant approximation of the parameters when compared to the reference model whilst allowing low-order model to achieve 40% to 50% computational efficiency than the reference one. Additionally, extensive simulations are carried to evaluate the proposed simplified model with solar radiation and identified model parameters. The developed simplified model is afterward validated with real data from a case study building where the achieved results clearly show a high degree of accuracy compared to the actual data.

The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

The conventional power system has been evolving towards a smart grid system for the past few decades. An integral step in successful realization of smart grid is to deploy renewable energy resources, particularly rooftop photovoltaic systems, at smart homes. With demand response opportunities in smart grid, residential customers can manage the utilization of their demand responsive appliances for getting economic benefits and incentives in return. In this regard, this paper proposes an effective home energy management system for residential customer to optimally schedule the demand responsive appliance in the presence of local photovoltaic and energy storage systems. For efficient home-to-grid energy transactions in home energy management system, the stochastic nature of photovoltaic power generation is modeled with the beta probability distribution function for solar irradiance. The main contribution of this paper is the application of polar bear optimization (PBO) method for optimally solving the scheduling problem of demand responsive appliances in home energy management system to minimize electricity consumption cost as well as peak-to-average ratio. The effectiveness of the proposed metaheuristic optimization technique is proven by performing different case studies for a residential consumer with different base load, uninterruptible deferrable, and interruptible deferrable appliances under a real-time energy price program. Comparative results with different metaheuristic techniques available in the literature show that the electricity consumption cost and peak-to-average ratio are effectively optimized using the proposed PBO algorithm.