Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I – Building modeling

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

The appropriate application of advanced control strategies in Heating, Ventilation, and Air-conditioning (HVAC) systems is key to improving the energy efficiency of buildings. Significant advances have been made in the past decades on model development to provide better control over the energy consumption of system components while simultaneously ensuring a satisfactory indoor environment in terms of thermal comfort and indoor air quality. Yet it is an ongoing challenge to select and implement the best-suited modeling technique for improving the control strategy of HVAC systems. For the development of modeling research it is important that the building research community is informed about the role, application, merits, shortcomings and outcomes of different modeling techniques used in HVAC systems. Even though several review articles have been published on modeling techniques, the weaknesses and strengths of these modeling techniques, along with performances of developed models associated with research studies, have rarely been identified. This study presents a critical review of current modeling techniques used in HVAC systems regarding their applicability and ease of acceptance in practice and summarizes the strengths, weaknesses, applications and performance of these modeling techniques. Additionally, the performance and outcome of some of the developed models used in real world HVAC systems have been discussed. From the extensive critical review it is evident that almost every model has a major/minor shortcoming generated from assumptions, unmeasured disturbances or uncertainties in some system properties. This review aims at highlighting the shortcomings of existing application-based research on HVAC systems, and accordingly, recommendations are presented to improve the performance of building HVAC systems.

Modeling techniques used in building HVAC control systems: A review

Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural network models for an open office

Information about the patterns that govern the energy demand and onsite generation can generate significant savings in the range of 15–30% in most cases and thus is essential for the management of commercial building energy systems. Predominantly, heating and cooling in a building as well as the availability of solar and wind energy are directly affected by variables such as temperature, humidity and solar radiation. This makes energy management decision making and planning sensitive to the prevalent and future weather conditions. Research attempts are being made using a variety of statistical or physical algorithms to predict the evolution of the building load or generation in order to optimise the building energy management. The response of the building energy system to changes in weather conditions is inherently challenging to predict; nevertheless numerous methods in the literature describe and utilise weather predictions. Such methods are being reviewed in this study and their strengths, weaknesses and applications in commercial buildings at different prediction horizons are discussed. Furthermore, the importance of considering weather forecasting inputs in energy management systems is established by highlighting the dependencies of various building components on weather conditions. The issues of the difficulty in implementation of integrated weather forecasts at commercial building level and the potential added value through energy management optimisation are also addressed. Finally, a novel framework is proposed that utilises a range of weather variable predictions in order to optimise certain commercial building systems.

R. Castañeda-Miranda

Papers

Modelling temperature in intelligent buildings by means of autoregressive models