Performance comparison of heating control strategies combining simulation and experimental results

Different heating system controllers for passive solar buildings are compared on two different buildings.The performance criterion combines energy performance and thermal comfort using the "costfunction" paradigm. The experimental facilities did not allow a direct experimental comparison by using two identical buildings. The controllers were implemented alternatively in one building and a performance comparison was obtained in two ways: first by identifying short periods that have similar driving variables (weather conditions and building occupancy) and comparing the experimental results obtained in both cases. The second method mixes experiments and simulation using a well-tuned model of the building and its occupants. This paper discusses the results obtained using the above methods and shows that both methods give consistent estimates of the difference between controllers, while the second method allows to extrapolate useful information from the limited data available

Modeling and Control of Reversible Air-to-water Heat Pumps

In this framework, the provision of modeling on a detailed thermodynamic modeling and the embedded architecture control is of paramount importance to achieve reliable control and operation. More specifically, the developed models should provide insights into (i) the ability of the RHP to quickly respond to the variation in the cooling and heating demand of the users (e.g., building load); (ii) the capability to adapt to the variation in the local while accounting for limits of the electric motor and the embedded controller, and (iii) the possibility to compare new control strategies aimed at improving the overall energy performance. The thesis compared different controls (e.g., proportional-integral for variable speed, and hysteresis controller for sequential control.) and they demonstrated that the modeling complexity of the system control has a significant impact on the key performance indicators, proving that this aspect should not be overlooked. For instance, for short-term operation, the modeling of the heat pump controller and the transient effects of the heat pump, such as cycling losses during start-up, are important

Environmental Technology Applications in the Retrofitting of Residential Buildings

The impact of buildings on the environment is nothing short of devastating. In recent years, much attention has been given to creating an environmentally friendly built environment. Nonetheless, it has been levied on new buildings. Residential buildings make up at least 80% of the built environment, most of which were built before any energy efficiency guidelines or regulations were introduced. Retrofitting existing residential buildings is a key yet neglected priority in effecting the transition to an environmentally friendly, sustainable built environment. It is pivotal to reducing a building’s energy consumption while simultaneously improving indoor environmental quality and minimizing harmful emissions. This Special Issue showcases studies investigating applications of environmental technology that is tailored to enhance the sustainable performance of existing residential buildings. It helps to better understand the innovations that have been taking place in retrofitting residential buildings, as well as highlighting many opportunities for future research in this field

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings

This is the publisher's version, also available electronically from http://www.mdpi.com/1996-1073/6/7/3548This study examines the performance and adaptability of Artificial Neural Network (ANN)-based thermal control strategies for diverse thermal properties of building envelope conditions applied to residential buildings. The thermal performance using two non-ANN-based control logics and two predictive ANN-based control logics was numerically tested using simulation software after validation. The performance tests were conducted for a two-story single-family house for various envelope insulation levels and window-to-wall ratios on the envelopes. The percentages of the period within the targeted ranges for air temperature, humidity and PMV, and the magnitudes of the overshoots and undershoots outside of the targeted comfort range were analyzed for each control logic scheme. The results revealed that the two predictive control logics that employed thermal predictions of the ANN models achieved longer periods of thermal comfort than the non-ANN-based models in terms of the comfort periods and the reductions of the magnitudes of the overshoots and undershoots. The ANN-based models proved their adaptability through accurate control of the thermal conditions in buildings with various architectural variables. The ANN-based predictive control methods demonstrated their potential to create more comfortable thermal conditions in single-family homes compared to non-ANN based control logics

Dynamic modeling, control and energy simulation of a solar-assisted hydronic space heating system in a multi-function building

A dynamic model of a solar-assisted hydronic space heating system for a multi-function building has been developed. The system performance under different control strategies and the model-based energy simulations studies have been conducted. The system consists of several components including a boiler, heat exchangers, flat-plate solar collectors, a water storage tank, baseboard heaters and a radiant floor heating system. The model consists of nonlinear differential equations which were programmed and solved using the MATLAB software. Two control strategies have been explored to compare the system performance: (i) a conventional PI control and (ii) a gain-scheduling adaptive (GSA) PI control. The simulation results indicate that the system performance under GSA PI control is better than the conventional PI control with respect to disturbance rejection and stability. An optimization problem was formulated and solved to study the energy performance of the system. Preliminary simulation results with assumed outdoor temperature profiles showed that the optimized set-point operating strategy contributes 7% and 14.87% to boiler energy saving in mild and warm day conditions compared with constant set-point strategy. One week energy simulations under actual weather conditions based on typical meteorological year (TMY) data have been conducted to investigate the percent contribution of solar energy to space heating. The simulation results show that the solar system contributes less energy during cold winter conditions such as in the month of December. However, it can reduce 16.94% of boiler energy supplied to the radiant floor heating system in the month of March. Besides, the implementation of the optimal GSA PI control strategy can result in higher solar fractions of 5.71% and 30.36% as compared to the base case PI control under cold (December) and mild (March) weather conditions, respectively

Model Predictive Control-based Surface Condensation Prevention for Thermo-active Building Systems (TABS): In Regard to the Partial Theoretical Model Approach

The potential risk of developing surface condensation keeps thermo-active building systems (TABS) from being applied in buildings located in partly warm and humid climate regions. This study presents a framework for model predictive control (MPC)-based surface condensation prevention that can avoid the surface condensation during the cooling periods when the TABS is in operation. Because MPC determines the input signal for the system not only based on the current states but also on the impact that the actions will have on the future states, it is suitable for anticipatory surface condensation control that must respond to both dynamic indoor condition changes and the time-delay in hygrothermal transfer in advance. Heat and moisture transfer dynamic models were developed for prediction of future states and these dynamic models were calibrated with the measured data to improve the surface condensation prediction accuracy. Based on future states predicted by the calibrated dynamic models, the MPC-based condensation prevention framework adjusts the surface temperature for the TABS in ways that ensure indoor thermal comfort and energy efficiency without the development of surface condensation. The proposed MPC-based surface condensation prevention framework reduced the surface condensation occurrence risk as well as the cooling energy even when the TABS is in operation under warm and humid climate regions. Given the growing demand for the TABS, the proposed MPC framework meets a critical need. By controlling the potential risk of surface condensation development, it can extend TABS use to an area in which climate conditions had made them infeasible.PHDArchitectureUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167944/1/deokoh_1.pd