Utilization of intelligent control algorithms for thermal comfort optimization and energy saving

Developing a Thermal Comfort Report Card for Building

In this work, HVAC (Heating, Ventilation and Air Conditioning) systems applied in university buildings with control based on PMV (Predicted Mean Vote) and aPMV (adaptive Predicted Mean Vote) indexes are discussed. The building’s thermal behavior with complex topology, in transient thermal conditions, for summer and winter conditions is simulated by software. The university building is divided into 124 spaces, on two levels with an area of 5931 m2, and is composed of 201 transparent surfaces and 1740 opaque surfaces. There are 86 compartments equipped with HVAC systems. The simulation considers the actual occupation and ventilation cycles, the external environmental variables, the internal HVAC system and the occupants’ and building’s characteristics. In this work, a new HVAC control system, designed to simultaneously obtain better occupants’ thermal comfort levels according to category C of ISO 7730 with less energy consumption, is presented. This new HVAC system with aPMV index control is numerically implemented, and its performance is compared with the performance of the same HVAC system with the usual PMV index control. Both HVAC control systems turn on only when the PMV index or the aPMV index reaches values below −0.7, in winter conditions, and when the PMV index or the aPMV index reaches values above +0.7, in summer conditions. In accordance with the results obtained, the HVAC system guarantees negative PMV and aPMV indexes in winter conditions and positive PMV and aPMV indexes in summer conditions. The energy consumption level is higher in winter conditions than in summer conditions for compartments with shading, and it is lower in winter conditions than in summer conditions for compartments exposed to direct solar radiation. The consumption level is higher using the PMV control than with the aPMV control. Air temperature, in accordance with Portuguese standards, is higher than 20 °C in winter conditions and lower than 27 °C in summer conditions. In Mediterranean climates, the HVAC systems with aPMV control provide better occupants’ thermal comfort levels and less energy consumption than the HVAC system with PMV control.

Energy-efficient personalized thermal comfort control in office buildings based on multi-agent deep reinforcement learning

In this article, two new algorithms of the redundant force branch of 6-PUS/UPU parallel robot are proposed. They are model predictive control combining with proportional, integral, and differential algorithm and fuzzy combining with model predictive control algorithm. The shortcoming of the traditional model predictive control algorithm is complex adjustment, large amount of calculation, the dynamic performance effect of the system. The proposed PID model predictive control algorithm can make the controller parameters adjustment more convenient. However, PID model predictive control algorithm can’t obtain good control performance under sudden change in situation. Combining model predictive control algorithm with fuzzy theory, fuzzy model predictive control algorithm has better anti-interference ability than PID model predictive control algorithm and can reduce predictive horizon length as possible as it can. Simulation results show that fuzzy model predictive control algorithm can effectively improve real-time performance of control system, the dynamic tracking performance and robustness than the traditional model predictive control and PID model predictive control algorithm.

Diffusion of bio-climatic building design techniques in Kenya: Impediments and opportunities

Kashgar, located in China’s far west, has a typical hot-arid climate. There are a large number of low-rise high-density Islamic housing in Kashgar old City. The thermal comfort in these buildings has not yet been adequately researched. In order to obtain a comprehensive understanding about the real indoor thermal environment and the residents’ summer thermal comfort in Kashgar old City, a field investigation of 138 residential buildings was conducted in the summer time, including onsite environmental parameter measurements and simultaneous surveys using subjective thermal comfort questionnaires. Based on a statistical regression analysis of 182 valid questionnaires, a summer thermal comfort model was established. The results show that the measured summer neutral temperature of the local people is 23.4°C, and the upper limit of acceptable summer temperature for 80% of the residents is 26.9°C. The summer thermal tolerance of the residents in the Kashgar old City is lower than expected and also lower than that of the residents in rural Turpan with the same climate features. The survey also shows that the residents in the Kashgar old City have a habit of transfering living spaces over time, and the frequency of passive behaviours is higher than those of active behaviours. The above results are believed to be potentially valuable for the revision of energy conservation policies and the design practice of residential buildings in the hot-arid climate region of China, and also for areas under similar climate conditions in the world.

Thermal comfort in educational buildings: The Classroom-Comfort-Data method applied to schools in Bogotá, Colombia

Energy-efficient fuzzy model-based multivariable predictive control of a HVAC system

The heating, ventilation, and air conditioning (HVAC) system is a major energy consumer in office buildings, and its operation is critical for indoor thermal comfort. While previous studies have indicated that reinforcement learning control can improve HVAC energy efficiency, they did not provide enough information about end-to-end control (i.e., from raw observations to ready-to-implement control signals) for centralized HVAC systems in multizone buildings due to the limitations of reinforcement learning methods or the test buildings being single zones with independent HVAC systems. This study developed a model-free end-to-end dynamic HVAC control method based on a recently proposed deep reinforcement learning framework to control the centralized HVAC system of a multizone office building. By using the deep neural network, the proposed control method could directly take measurable parameters, including weather and indoor environment conditions, as inputs and control indoor temperature setpoints at a supervisory level. In some test cases, the proposed control method could successfully learn a dynamic control policy to reduce HVAC energy consumption by 12.8% compared with the baseline case using conventional control methods, without compromising thermal comfort. However, an over-fitting problem was noted, indicating that future work should first focus on the generalization of deep reinforcement learning.

A review of different strategies for HVAC energy saving

Thermal comfort markedly impacts our health, well-being and work productivity. This article is a review of practices generally adopted to quantify human thermal comfort in buildings. The review indicates that there is significant variation in comfort requirements due to diversified socio-cultural set-up and local adaptive behaviour. Thus, the localised thermal comfort models need to be developed to identify the actual comfort requirements helpful in drafting new local comfort standards. The study justifying the relationship between thermal comfort and indoor air quality are scant and need to be explored as such relationships are greatly dependent on occupant’s adaptive behaviour. Further, the interdisciplinary research on thermal comfort which not only helps in real-time assessment but also covers other critical aspects like building architecture and energy consumption is lacking in the literature. Moreover, this review paves way for research on thermal comfort in countries where high building stock is expected in future.

A toolchain to evaluate the impact of urban heat island and climate change on summer overheating at district level

Thermal, microstructural and numerical analysis of hempcrete-microencapsulated phase change material composites

Maintaining both indoor air quality (IAQ) and thermal comfort in buildings along with optimized energy consumption is a challenging problem. This investigation presents a novel design for hybrid ventilation system enabled by predictive control and soft-sensors to achieve both IAQ and thermal comfort by combining predictive control with demand controlled ventilation (DCV). First, we show that the problem of maintaining IAQ, thermal comfort and optimal energy is a multi-objective optimization problem with competing objectives, and a predictive control approach is required to smartly control the system. This leads to many implementation challenges which are addressed by designing a hybrid ventilation scheme supported by predictive control and soft-sensors. The main idea of the hybrid ventilation system is to achieve thermal comfort by varying the ON/OFF times of the air conditioners to maintain the temperature within user-defined bands using a predictive control and IAQ is maintained using Healthbox 3.0, a DCV device. Furthermore, this study also designs soft-sensors by combining the Internet of Things (IoT)-based sensors with deep-learning tools. The hardware realization of the control and IoT prototype is also discussed. The proposed novel hybrid ventilation system and the soft-sensors are demonstrated in a real research laboratory, i.e., Center for Research in Automatic Control Engineering (C-RACE) located at Kalasalingam University, India. Our results show the perceived benefits of hybrid ventilation, predictive control, and soft-sensors.

A robust heating, ventilation, and air conditioning (HVAC) system is needed to maintain a healthy and comfortable indoor environment. However, HVAC systems are responsible for significant energy usage in the United States, and enhancing current systems and implementing additional HVAC sensing are primary strategies for reducing energy consumption. This research developed an HVAC control algorithm (CA) that optimized ventilation operations within a conference room in the University of Virginia Link Lab. Using indoor air quality (IAQ), occupancy, weather, and HVAC operation data streams, the CA recommended a decision to ventilate or not ventilate the conference room every 15 minutes by comparing the cost of lost occupant productivity due to poor IAQ to the energy cost of ventilating the space. The ventilation decision with lower total cost was recommended. This project addressed scheduling inefficiencies of the current HVAC control system, which operates at full power throughout the day regardless of occupancy status. The CA reduced ventilation during unoccupied periods. The CA was tested over two months of historical data from October to December 2021 and recommended ventilating the conference room 15.13 percent of the time. During the same period, the standard system ventilated the conference room 49 percent of the time. Energy savings due to decreased operation were considerable and averaged 424 dollars per month, although these energy savings came at the cost of lost occupant productivity totaling 522 dollars per month. Future work on lost occupant performance will more accurately model the effects of reduced ventilation. However, annual energy savings of 5,000 dollars from a single conference room is encouraging, and scaling a similar CA to consider a set of rooms or an entire floor of a building could result in substantial energy conservation.