Temperature Set Point Research Articles

Energy Performance of Room Air-Conditioners and Ceiling Fans in Mixed-Mode Buildings

Studies show that people can tolerate elevated temperatures in the presence of appreciable air movement (e.g., from using ceiling fans). This minimises the use of air-conditioners and extends their set-point temperature (Tset), resulting in energy savings in space cooling. However, there is little empirical evidence on the energy savings from using ceiling fans with Room Air-Conditioners (RACs). To address this gap, we analysed the energy performance of RACs with both fixed-speed compressors and inverter technology at different set-point temperatures and ceiling fan speed settings in 15 residential Mixed-Mode Buildings (MMBs) in India. Thermal comfort conditions (as predicted by the Indian Model for Adaptive Comfort-Residential (IMAC-R)) with minimum energy consumption were maintained at a set-point temperature (Tset) of 28 and 30 ∘C and a fan speed setting of one. Compared with a Tset of 24 °C, a Tset of 28 and 30 °C resulted in energy savings of 44 and 67%, respectively. With the use of RACs, a configuration with a minimum fan speed was satisfactory for an optimal use of energy and for maintaining the conditions of thermal comfort. In addition, RACs with inverter technology used 34–68% less energy than fixed-speed compressors. With the rising use of RACs, particularly in tropical regions, the study’s outcomes offer a significant potential for reducing space-cooling energy consumption and the resultant greenhouse gas (GHG) emissions.

A multimodal cooling garment for personal thermal comfort management

Intelligent thermoregulation garments for personal thermal management (PTM) are essential to improve thermal comfort and reduce energy consumption. However, current attempts are unable to simultaneously address the aforementioned demands because of constraints including the environment, energy consumption, and wearing comfort. Here, we report a multi-effect cooling garment (MECG) with synergism regulation mechanism for personal thermal comfort management. By decently integrating radiative cooling fabrics, thermoelectric cooling devices, and heat transfer channels on textiles, MECG exhibits improved wearability and excellent personalized thermoregulation ability, not merely cooling function. Studies have demonstrated that MECG can greatly improve human thermal comfort and lessen the hot and sticky discomfort that comes with high temperatures. It is wirelessly controllable and can raise the air conditioning system's summertime setpoint temperature by about 4 °C. Owing to its exceptional thermal management performance and personalized thermoregulation ability, we expect the MECG concept to provide promising design guidelines for next-generation intelligent thermoregulation garments.

Analysis of factors influencing energy-saving advantages of outside and inside insulation of exterior walls in intermittent air-conditioned buildings

An effective approach to reducing building energy is determining the proper insulation location of the exterior walls. Two conflicting conclusions were found in previous research that focused on the optimal insulation location of exterior walls in buildings with intermittently operated convective air systems (CASs). Why conflicting conclusions are produced and which factors influence them have not yet been answered. Thus, this paper analyzes the generation of the transmission load through the exterior walls with outside and inside insulation, and accordingly evaluates the parameters that might affect the energy-saving advantage of two insulation locations. An analytic model is utilized to calculate the heat transfer through the exterior walls. Results show that the heat stored in exterior walls’ inner layer during CAS off-periods is the key to determining the relative energy-saving of two insulation locations. Furthermore, the relative energy-saving increases with thermal capacity of exterior envelopes and air change rate but decreases with the increase in thermal capacity of interior envelopes and setpoint temperature. Based on these, a predictive model is devised to determine the proper insulation location of exterior wall. A critical daily sol-air temperature range beyond which the relative energy-saving advantage transitions is found using the model.

Natural cooling solution for thermally conditioning bus stops as urban climate shelters in hot areas: Experimental proof of concept

In response to the challenges posed by climate change and the growing heat patterns in cities, this study addresses the design and implementation of climate shelters integrated into public transport stops through the installation of surface radiant cooling modules. The aim is to improve the thermal comfort of short-stay users in cities in hot, dry climates. To this end, a fully sensorized thermal camera was used in a controlled setting to simulate real outdoor climate conditions and evaluate the efficacy of the radiant system. The prototype was modelled and characterized by means of in situ measurements, paying special attention to the temperature and radiant heat flux. The radiant modules maintained a temperature between 18 °C and 20 °C, even under oppressive conditions, with over 50% of the cooling flux due to radiation. The radiative performance of the prototype did not increase proportionally with increased water flow, stabilizing at high flows. The analysis of the thermal comfort index showed a decrease in feelings of discomfort of between 40% and 50% when the modules reached the set point temperature. The study shows that implementing these climate shelters in public transport stops such a bus shelters is feasible in hot, dry climates. This may be an effective strategy for mitigating the effects of climate change in urban areas and can be applied in any city that suffers from recurring heatwaves, thereby improving living conditions for city goers.

Energy flexibility of commercial buildings for demand response applications in Australia

Demand response (DR) is widely recognized as an important mechanism in the Australian electricity market, though large-scale uptake in commercial buildings is yet to occur, in part due to the difficulty of characterising the resource. This paper describes a bottom-up physics-based approach to characterise the DR potential of three types of commercial buildings (schools, offices, and data centres) under a global set-point temperature offset strategy. Representative models are calibrated with energy meter data and parametric analysis is used to assess sensitivity to different building, operating and system parameters. Parametric equations are provided for relative DR potentials as functions of temperature and time of day. School buildings were found to have the highest relative DR potential (∼40–45%) for ambient temperatures over 30 °C, followed by data centres (∼20–30%) and offices (∼20%). Location has the strongest relative influence for school and office buildings and equipment energy intensity for data centres. The Australia-wide combined DR potential for school, office and data centres is estimated to be between 551 and 647 MW. Office buildings have the highest aggregate potential at between 1.5 and 1.7 times that of school buildings and between 9 and 11 times that of data centres.

Decoupling the complexities of air-conditioning cooling energy use in express hotels by data mining approaches

The air-conditioning (AC) energy use in express hotels is stochastic with the high coupling relationships amongst AC usage, indoor temperature and energy consumption. Such complexities and stochasticity make it hard to facilitate energy saving with clear effect on indoor environment. However, lacking analyses of high-resolution occupants’ energy use makes it difficult to achieve such goals due to the split form of ACs and various thermal comfort of guests in express hotels. Therefore, this study made a serial analysis on the AC energy use in a more detailed scope. The stochastic AC usage, indoor temperature and AC energy consumption were quantified by proposed typical patterns with the cluster method. The stochasticity was described by four typical patterns for each aspect. After the quantifications, the relationships amongst these three aspects were decoupled by the proposed energy use decoupling model. Two data mining methods, namely, random forest method and decision tree method, were employed to achieve this purpose, respectively. With these models, the impacts of each variable on AC energy consumption and explicit relationships of operation rules for management are presented. Strictly limiting set point temperature higher than 23°C is the effective way to save energy for most of AC usage patterns. This study can provide a deeper understanding of AC energy use in express hotels, and benefits energy saving and facility operation in express hotels.

Active disturbance rejection control for the reheated steam temperature of a double-reheat boiler

As a state-of-the-art thermal power generation technology with high efficiency and low emissions, the double-reheat boiler is facing many control challenges on the reheated steam temperature (RST), such as nonlinearity, coupling, and large inertia lag. However, the widely used Proportional–Integral–Derivative (PID) may not efficiently handle these challenges. To this end, a control scheme based on active disturbance rejection control (ADRC) is synthesized, after analyzing the nonlinearity of the system and the decoupling of a control structure for the mean and deviation of temperature (MDT) in the frequency domain. The distinctive features of the proposed scheme are (i) the MDT structure is utilized to decouple; (ii) Modified active disturbance rejection control (MADRC) is used to handle large inertia lag; (iii) the compensation part of MADRC is adaptive to load, and the temperature setpoints and feedforward modules are optimized to reduce nonlinearity. The superiority of the proposed control system is examined by simulation experiments. Lastly, ADRC was successfully applied to the practical RST system, showing better temperature control quality than the initial manual mode. The analysis and practice in this paper demonstrate the feasibility of ADRC for the RST system and offer guidance for further study on RST control.

Air-conditioning usage behaviour of the elderly in caring home during the extremely hot summer period: An evidence in Chongqing

Using air conditioners to cool down the indoor thermal environment is necessary to avoid the elderly exposing themselves to hot conditions in extreme weather. Considering the frequency occurrence of heatwave events, this study investigated air-conditioner operation behaviours and indoor thermal condition features during extremely hot summer periods to understand the elderly's thermal demand and their behaviour patterns of using air conditioners. Fifteen elderly residents from a caring home in Chongqing were recruited, and their air-conditioners’ historical operation data and indoor and outdoor temperatures were continuously measured and retrieved from the air-conditioner manufacturing data warehouse. The results indicate that the elderly subjects were active in adjusting their air-conditioner settings with multiple daily actions of turning-on and setpoint-changing behaviours in the extremely hot summer period. The air-conditioner temperature setpoint preferred by the elderly subjects was between 27 °C and 30 °C, resulting in the indoor temperature mainly remaining in the range between 28 °C and 32 °C, which presented a negative relationship with the outdoor temperature. The elderly's daily air-conditioner operation in the extremely hot summer period can be clustered into four groups, including inactive-usage, afternoon-usage, night-usage and all-day-usage groups. The inactive usage group with short-term and discrete air-conditioner operation would prefer a fast-cooling setting, whereas the all-day usage group showed a higher setpoint-change frequency, indicating the dynamic cooling demand may exist in the elderly's long-term air-conditioner daily usage.

MF^2: Model-free reinforcement learning for modeling-free building HVAC control with data-driven environment construction in a residential building

Reinforcement Learning (RL) has advanced energy-efficient control of building Heating, Ventilation and Air Conditioning (HVAC) systems. Constructing a suitable RL environment for buildings is a crucial challenge. Compared to widely-used simulation-based environments, data-driven approaches offer higher training efficiency but face convergence difficulties due to influential factors, limiting their current application.To explore data-driven construction of RL environments for building HVAC systems, this study proposes two strategies for controlling room temperature setpoints in a residential building. XGBoost and Long Short-Term Memory Network (LSTM) are trained for energy consumption and room temperature prediction. One strategy predicts parameters for on-off states, while the other for power-on states. The XGBoost models are integrated into an OpenAI Gym environment. The first strategy achieves 0.8634 R2 and 0.2423 Root Mean Squared Error (RMSE) for energy consumption prediction. The R2 of room air temperature models are approximately 0.99 and the RMSE are lower than 0.31. The second strategy achieves 0.9181 R2 and 0.1042 RMSE for energy consumption prediction and similar performance for room temperature prediction. Deep Q-learning (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are separately trained using these environments. Results show that the first strategy fails to induce the correct training of RL models, while the second strategy successfully induces a useable DDPG model for controlling building HVAC systems but fails to induce a useable DQN model. We analyze the reasons behind these observations. Compared to the original room temperature setpoint method, the DDPG-based HVAC control logic achieves a 10.06% energy-saving effect while ensuring comfort.

Real-time energy consumption prediction method for air-conditioning system based on long short-term memory neural network

With increasing energy consumption, how to achieve the energy-saving operation of air-conditioning systems is crucial for improving the energy efficiency of buildings. The accurate and reliable energy consumption prediction of air-conditioning systems can be useful for optimizing the energy supply and equipment operation strategies. However, most existing studies focus on the prediction of the long-term energy consumption of air-conditioning systems, which usually exceeds the individual control time steps of the air-conditioning system. If the energy consumption of air-conditioning systems in the next control step can be predicted, then the least energy-consuming operating strategy can be selected at the beginning of each control step by relying on the predicted energy consumption, thereby achieving greater energy conservation. This paper proposes a real-time energy consumption prediction model for air-conditioning systems based on a long short-term memory neural network. The correlation between the energy consumption of air-conditioning system and input variables can be obtained by calculating the Spearman correlation coefficient. The predicted model results show that the normalized root mean square error of the prediction model is 0.0429 when the indoor air temperature, condenser inlet temperature, and historical energy consumption are set as inputs to predict the energy consumption of the air-conditioning system for the next 15 min. Meanwhile, the energy consumption at a certain indoor temperature setpoint can be predicted by the prediction model to aid in selecting a more appropriate indoor temperature setpoint, which can reduce energy consumption by optimizing the operation strategy of the air-conditioning system at each control step.

Safe operation of online learning data driven model predictive control of building energy systems

Model predictive control is a promising approach to reduce the CO2 emissions in the building sector. However, the vast modeling effort hampers the widescale practical application. Here, data-driven process models, like artificial neural networks, are well-suited to automatize the modeling. However, the underlying data set strongly determines the quality and reliability of artificial neural networks. In general, the validity domain of a machine learning model is limited to the data that was used to train it. Predictions based on system states outside that domain, so-called extrapolations, are unreliable and can negatively influence the control quality.We present a safe operation approach combined with online learning to deal with extrapolation in data-driven model predictive control. Here, the k-nearest neighbor algorithm is used to detect extrapolation to switch to a robust fallback controller. By continuously retraining the artificial neural networks during operation, we successively increase the validity domain of the artificial neural networks and the control quality.We apply the approach to control a building energy system provided by the BOPTEST framework. We compare controllers based on two data sets, one with extensive system excitation and one with baseline operation. The system is controlled to a fixed temperature set point in baseline operation. Therefore, the artificial neural networks trained on this data set tend to extrapolate in other operating points. We show that safe operation in combination with online learning significantly improves performance.

What must be considered in winter strawberry production under LEDs in Iceland?

ABSTRACT Supplementary lighting is essential to maintain year-round production in Iceland due to the extremely low natural light level in winter. In this research, the effects of high-pressure vapour sodium lamps (HPS) are compared to light emitting diodes (LED), both with similar photosynthetic photon flux density (PPFD). Strawberries (Fragaria x ananassa cv. ‘Sonata' and cv. ‘Magnum') were grown either under HPS lights or LEDs and 16°C/8°C (day/night). However, in the second winter, the day temperature was increased to 19°C under LEDs. The results showed that under the same temperature set points, the development of the flowers and the harvest was delayed by two weeks under LEDs due to a lower leaf, substrate and air temperature. However, when temperature set points were adapted, no delay under LEDs was observed. LEDs did not lead to higher yield, but to a higher energy use efficiency, while light use efficiency behaved contrary. Economic calculations clearly demonstrate that it is not justified to switch from HPS lights to LEDs. Instead, it is rather recommended to emphasise a high-yielding variety like Sonata in winter-growing of strawberries.

Effects of fin positioning on the thermal performance of a phase change material–filled heat sink with horizontal fins

We conducted numerical simulations to investigate the influence of the horizontal fin arrangement on the thermal performance of a phase change material (PCM)-filled heat sink. Six horizontal fin positions were considered to compare and analyze heat transfer features and variations in the base temperature of the heat sink. The enthalpy–porosity method was used to investigate the phase change process of the PCM and the interface between the PCM and air. The working fluid inside the heat sink was tracked using a volume of fluid model. Heat transfer phenomena during the solid PCM–melting process were examined. The time required to reach the set point temperature (SPT) and storage ratio have been reported for all fin configurations. When the horizontal fins were aligned at the center of the heat sink, the time taken to attain the lowest SPT was the longest and the storage ratio was the highest. In the presence of horizontal fins, the energy storage rate was improved by 3.8 % compared to the case without horizontal fins. In addition, a difference of 2.7 % energy storage ratio appeared depending on the arrangement of horizontal fins, revealing that there exists an arrangement of horizontal fins to effectively store the thermal energy input to the heat sink. The results herein can benefit the thermal management of heat sinks encountered when cooling electronics.

Servo and Regulatory Response of an Industrial Fluid Catalytic Cracking (FCC) Unit under Fuzzy Logic Supervisory Control

A self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented in this paper. The controller has been applied to a control study of Fluid Catalytic Cracking unit (FCCU) riser temperature, and regenerator temperature respectively. Comparison between the performance of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID (12.6%) / Fuzzy-PID (6%)); settling time (PID (80 seconds) / Fuzzy-PID (20seconds)); Integral absolute error (PID (14.29) / Fuzzy-PID (8.63)); integral square error (PID(6.713). Fuzzy-PID (4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)) . Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

Strategies to minimize fallback pigs in the nursery

Preweaning strategies to minimize fallback pigs in the nursery include cross fostering, creep feeding, and weaning an older pig. Postweaning strategies to minimize fallback pigs in the nursery include optimum barn temperature and ventilation set points, easy access to feed and water, and proactive placement strategies. Phase-feeding programs to match the nutrient requirements and digestive abilities of weaned pigs are also crucial. Managing fallback pigs can be accomplished by minimizing drafts in removal pens, providing supplemental heat sources, having multiple feed access points by providing gruel and mat feed, and ensuring more intensive observations of pigs in removal pens.

Reinforcement learning-based demand response strategy for thermal energy storage air-conditioning system considering room temperature and humidity setpoints

Heating, ventilation, and air-conditioning systems significantly influence demand response (DR) actions. In the literature, many studies have investigated the reduction of sensible loads through DR events by increasing temperature setpoints while satisfying indoor comfort levels. However, less consideration has been given to latent loads. Therefore, proximal policy optimization reinforcement learning (RL) algorithms are adopted to simultaneously control indoor temperature and humidity (T&H) setpoints to reduce sensible and latent loads. An EnergyPlus-Python joint simulation platform was created for the temperature-humidity independent control system. DR strategies based on RL, active thermal energy storage, and time-of-use electricity prices are formulated to find the optimal indoor T&H setpoints, considering environmental constraints, comfort levels, and energy consumption. The results showed that in the optimal experimental strategies implemented based on negative feedback regulation, savings of 29.15 % on electricity consumption were achieved during DR periods and 46.11 % savings on the daily operating costs. Moreover, a simulation strategy based on feed-forward regulation was developed and found to reduce electricity consumption by 6.92 % during DR periods. Compared with a fixed T&H setpoint, the developed strategy meets the preferences of different groups of people for thermal comfort and increases the system's flexibility.

Ammonia as an SOFC Fuel

The conventional production route of ammonia relies on natural gas as a source of hydrogen and is thus very CO2 intensive; almost 2% of global anthropogenic CO2 emissions stems from ammonia synthesis. This will have to change over the next two to three decades, where a “green route” to ammonia must be deployed to ensure required emission reductions.The annual production of ammonia is currently around 180 million tons of which ~80% is used for fertilizer production. This will likely increase due to increased fertilizer demand, but also because ammonia is a promising energy vector and a potential green fuel for the shipping sector. The expected energy demand for global shipping by 2050 is in the order of ~15 EJ [1]. It will be difficult to cover such a large demand by bio-mass derived fuels (e.g. methanol) considering global limitations in sustainable biomass resources and the needs of other sectors like aviation, heavy transport and chemical industry [1]. Hence, for the green transition efficient and sustainable routes to green ammonia are important, as will be efficient technologies to convert ammonia to electricity.Ammonia is an excellent fuel for SOFCs as previously reported in several studies [2], [3]. We have in the framework of the project “SOC4NH3” [3] investigated the impacts of firing an SOEC with ammonia in terms of its impact on overall losses, the cell degradation mechanisms as well as potential benefits.SOEC durability, when operating on ammonia, was followed over extended test periods between 2000 and 6000 hours at single cell level at different temperatures and current densities. The cells were Ni/YSZ-supported thin electrolyte cells of a 5*5 cm2 foot-print. The degradation rates observed when feeding with ammonia were very similar to degradation rates observed for the same cells when operated in a 1:3 gas mixture of N2/H2.Also the detailed electrochemical performance was explored at different temperatures using impedance spectroscopy. An example of impedance spectra obtained when feeding ammonia to the cell or a N2/H2 gas mixture at ca. 850 oC and 675 oC is presented in Figure 1. When feeding ammonia a cooling is observed due to the endothermic nature of the cracking reaction. This leads to a slight increase in both series resistance and polarization resistance. Therefore, the impedance responses in the ammonia experiments is in the Figure compared to responses obtained in N2/H2 at a slightly lower furnace set-point temperature (5-10 oC lower). Evidently, at around 850 oC, the only effect on the impedance response of feeding ammonia directly is the slight reduction in temperature; the N2/H2 and NH3 responses are practically identical when comparing characteristics at furnace set-point temperatures of 845 oC and 850 oC for N2/H2 and NH3, respectively. In contrast, if the same experiment is conducted at around 675 oC a difference between the response in ammonia and the response in N2/H2 is observed. The difference in behavior cannot be compensated by just lowering the furnace temperature a bit to simulate the impact of a cooling. The impedance at high frequency in ammonia is clearly much larger than for the N2/H2 feed. Note here, that the Rs in the two different feeds is practically identical. Hence, we have to conclude that there is a direct negative effect of the presence of ammonia at the electrode on the hydrogen oxidation kinetics. The effect is stronger than what can be ascribed to cooling and dilution of the H2. The details will be discussed in the paper.SOFC operation on ammonia and its impact at stack level was also explored. Potential advantages of ensuring that a part of the ammonia cracks inside the stack, as opposed to up-stream the stack, was addressed by modelling. Finally, a TSP1 SOFC stack from Topsoe A/S was operated with some internal ammonia cracking. The results will be presented and discussed to elucidate benefits and limitations in operating SOFC stacks with internal cracking of ammonia. Acknowledgment The work was supported by the EUDP-project SOC4NH3; Grant Agreement 64018-0546. References MarEfuel Project report; “Electro-fuels for long range maritime transport”, 2021. https://orbit.dtu.dk/en/projects/electro-fuels-for-long-range-maritime-transport, available from Orbit DTU. J.B. Hansen, J. Madsen, J.U. Nielsen, N. Christiansen, Proceedings European SOFC Fuel Cell Forum, Luzern, Switzerland (2010), and A. Hagen. Risø International Energy Conference 2007, Risø-R-1608(EN) 3. John Bøgild Hansen and Peter Vang Hendriksen 2019 ECS Trans. 91 2455. Figure 1

Numerical Evaluation of the Benefits Provided by the Ground Thermal Inertia to Urban Greenhouses

Communities operating urban greenhouses need affordable solutions to reduce their heating consumption. The objective of this study was to compare the ability of different simple ground-based solutions to reduce the heating energy consumption of relatively small urban greenhouses operated all year round in a cold climate. An urban greenhouse located in Montreal (Canada) and its thermal interactions with the ground were modeled with the TRNSYS 18 software. The following greenhouse scenarios were simulated: partially insulating the walls, partially burying the greenhouse below the ground level, reducing the inside setpoint temperature, and using an air–soil heat exchanger (ASHE) or a ground-coupled heat pump (GCHP). The heat exchangers for the last two cases were assumed to be located underneath the greenhouse to minimize footprint. The results showed that reducing the setpoint temperature by 10 °C and burying the greenhouse 2 m below the surface has the most impact on fuel consumption (−33% to −53%), while geothermal systems with a limited footprint (ASHE and GCHP) can reduce the fuel consumption by 21–35% and 18–27%, respectively, depending on the soil thermal conductivity and ground heat injection during summer. The scenarios do not provide the same benefits and have different implications on solar radiation availability, growth temperature, electrical consumption, and operation that must be considered when selecting a proper solution.

Multi-objective optimization of window opening and thermostat control for enhanced indoor environment quality and energy efficiency in contrasting climates

In this study, a triple-objective model-based optimization of a controlled opening window's specifications and thermostat control setpoints was investigated. Numerical simulations were performed employing EnergyPlus, and multi-criteria optimization of objective functions and decision parameters was accomplished by jEPlus + EA using Non-dominated Sorting Genetic Algorithm (NSGA-II). Controlled window optimization was carried out for a typical classroom, and the results were evaluated for two contrast climates. The decision variables are indoor temperature set points for opening control, thermostat set points, and the window opening area. The annual energy demand, the average CO2 concentration, and the predicted percentage of dissatisfaction (PPD) were also regarded as objective functions to be simultaneously minimized. Max-min normalization and selecting final answers from the Pareto front were performed by the weighted sum method. By operating the suggested parameters, executing the optimized control strategy on the window opening resulted in indoor environmental quality improvement and a substantial decrease in energy demand, which had been selected as objective functions. The results show that using a controller based on indoor temperature instead of time significantly improved indoor air quality but greatly increased energy consumption. By performing optimization, in more than 50% of the time, all objective functions were controlled simultaneously for both selected climates. In total, the optimized window and thermostat controller performed desirable in both climates.

Extended Kalman filter based extremum seeking algorithm for model-free control of building thermal environment under uncertainty

Online optimization of building thermal environment is no trivial task due to the complex and uncertain indoor thermodynamics. In this study, an extended Kalman filter-based extremum seeking control (EKF-ESC) strategy for model-free control of thermal environment under uncertainty is proposed. The EKF-ESC uses extended Kalman filter for estimating gradient of the objective function of the extremum seeking controller, which enables real-time optimization of thermal environment with fast convergence time. In addition, a co-simulation platform is developed by combining EnergyPlus and Simulink/Matlab for validating effectiveness of the EKF-ESC strategy for controlling thermal environment. Our study demonstrates that the EKF-ESC strategy has good adaptivity to uncertainties resulting from changes in weather, electrical equipment and occupancy. Moreover, it is shown that the EKF-ESC is robust to measurement noise. In addition, comparison of convergence performance among EKF-ESC, adaptive ESC, and classical ESC is conducted. It is shown that the EKF-ESC based control strategy has the fastest convergence time towards indoor temperature setpoints. Compared with the classical ESC and adaptive ESC schemes, about 77% and 55% reduction of the convergence time are realized through EKF-ESC. According to the research results, it could be inferred that EKF-ESC strategy has the potential for effective building thermal management under uncertainty with fast convergence time.