Cooling Temperature Set Points Research Articles

Building energy-saving mechanism for indoor cooling temperature set-point with different envelope: A case study in Guangzhou

The temperature set-point of air conditioning system is a crucial parameter for behavior adjustment. Nevertheless, the thermal characteristics of building envelopes are different at varied construction ages. In this context, there are already several systematic studies demonstrating the differences in energy savings as a result of raising the same temperature set-point for air conditioning. In this study, an office building was used under four assumed envelopes representing different construction ages, and the characteristic temperature method was applied to simulate the hourly dynamic air-conditioning load before and after the set temperature rise of 1°C presenting monthly and annual cooling consumption. We then explored the energy-saving effect and difference in the mechanism of air conditioning with increasing indoor temperature set-point. The results showed that, provided that the temperature set-point is increased by 1°C under the same climatic conditions, the worse the thermal performance of the envelope, the higher the annual air-conditioning energy and the higher the annual energy-saving rate of air conditioning, which indicates that the behavioral energy-saving guidance for the groups with the poor performance of the older envelope has more immediate effects. The hourly load reduction of different envelopes at the outdoor temperature between 26°C and 27°C constitutes the behavioral energy-saving effect, which is the main contribution of air-conditioning energy saving (58 – 79%), while the energy-saving effect contribution of temperature difference reduction is secondary. The newer the construction age, the better the performance of the envelope, the smaller the hourly load reduction amount of the building, and the smaller the relative energy-saving rate, but its energy-saving behavior in the total energy saving ratio is greater, and the contribution of the energy-saving behavior is greater. The results of this study can provide a reference for guiding occupant energy-saving behavior and standard formulation.

Global sensitivity analysis and optimisation of design parameters for low GHG emission lifecycle of multifamily buildings in India

In India, nearly 25% of citizens live in slums, unfit for a decent living. Replacing the existing residential building stock and the rapid development of the economy will significantly increase energy consumption and related greenhouse gases (GHG) emissions in the coming decades. Consequently, there is a high need of developing low lifecycle GHG emission, affordable and comfortable multifamily building designs, which can be widely implemented in the Indian construction market.The objective of this article is first to explore the most influential design parameters of the multifamily building located in the in: warm and humid (Bhubaneswar), hot and dry (Jodhpur), and composite (New-Delhi) climate zones of India on life cycle GHG emissions and indoor thermal comfort, and secondly, to perform a multi-objective optimisation considering the life cycle GHG emissions, life cycle cost and initial material investment cost based on the set of the most sensitive design parameters. The study combines a two-step global sensitivity analysis based on the Morris and Fast method with a multi-objective genetic algorithm integrated into one framework based on the parametric multifamily building model with extensive building performance simulations.The global sensitivity analysis results indicated that for all investigated locations: the apartment’s floor area, equipment load, windows-to floor ratio, mechanical ventilation airflow, and cooling temperature setpoint were the most influential design parameters in relation to the lifecycle GHG emissions. Finally, based on the multi-objective optimization, significant reductions in the range of 62–75% in terms of life cycle GHG emissions and 40–54% in terms of life cycle cost were achieved compared to the baseline 7-storey multifamily design scenario based on the minimum requirements of the Indian energy conservation code. At the same time, the initial material investment cost was 25–34% higher. The optimal set of the design strategies, resulting in the lowest life cycle GHG emissions and life cycle cost was found to be minimisation of the apartment’s floor area and windows to floor ratio, maximisation of the on-site renewable energy use, and design of a mechanical ventilation system combined with ceiling fans thus enabling the energy-efficient and thermally comfortable operation of the multifamily building with a high cooling temperature setpoint.

Investigation on typical occupant behavior in air-conditioned office buildings for South China’s Pearl River Delta

The excessive simplification of occupant behavior is considered as the most important factor that affects the uncertainty of building performance simulation, thus affects the reliability and generalizability of simulation-based design and forecast. In this paper, occupant behavior in air-conditioned office buildings of the Pearl River Delta (PRD) region was investigated and defined. Copies of 873 questionnaires about the occupant behavior in air-conditioned office buildings in the PRD region were collected to study the relationship between indoor environment quality and adaptive behaviors. Eight typical office occupant schedules were defined via K-means clustering method. A probability prediction model of cooling temperature set-point was established by using the Ordinal Logistic Regression method. According to the different control modes of air conditioning, window, blind and lighting equipment, four types of typical behavior patterns were proposed using the K-prototype clustering method, which could be developed into 20 typical occupant behavior styles of office buildings in the PRD region.

Response Surface Method to Calculate Energy Savings Associated with Thermal Comfort Improvement in Buildings

In developed countries, a large part of the building stock in 2050 will consist of currently existing buildings. Consequently, in order to achieve the objectives in terms of energy efficiency in the building sector we must consider not only new infrastructures but also the old ones. A reduction in energy consumption for climate control of between 50 and 90% can be achieved by rehabilitation and the implementation of different energy efficiency measures. Currently, these measures to reduce energy consumption and associated CO2 emissions can be modelled using computer tools. However, high precision and detail of thermal behaviour models through simulations can mean a great computational cost for companies, which results in a blockage of servers and workers. In this paper, the Response Surface Methodology (RSM) is presented as an innovative methodology for the simplification of models for calculation of the energy savings associated with thermal comfort improvement in buildings. A single-family house model, located in three different climates, is presented as a case study in order to validate the proposed methodology. Different scenarios were simulated, addressing heating and cooling temperature set points and external wall insulation represented by the transmittance (U-value). Results obtained from energy simulation using Design Builder were contrasted against those estimated from the simplified model extracted from the RSM analysis. The results revealed a deviation lower than 3% when comparing both methods. Therefore, the simplified mathematical prediction models are demonstrated to be suitable for the study of the energy performance of buildings, saving computational time, costs and associated human resources.

Cooling energy savings and occupant feedback in a two year retrofit evaluation of 99 automated ceiling fans staged with air conditioning

Controlled air movement is an effective strategy for maintaining occupant comfort while reducing energy consumption, since comfort at moderately warmer temperatures requires less space cooling. Modern ceiling fans provide a 2–4 °C cooling effect at power consumption comparable to LED lightbulbs (2–30 W) with gentle air speeds (0.5–1 m/s). However, very limited design guidance and performance data are available for using ceiling fans and air conditioning together, especially in commercial buildings. We present results from a 29-month field study of 99 automated ceiling fans and 12 thermostats installed in ten air-conditioned buildings in a hot/dry climate in California. Staging ceiling fans to automatically cool before, and then operate together with air conditioning enabled raising air conditioning cooling temperature setpoints in most zones, with overall positive occupant interview and survey responses. Overall measured cooling season (April– October) compressor energy savings were 36%, normalized by floor area served (41% during summer peak billing hours). Weather-normalized changes in zone energy use varied from 24% increase to 73% decrease across 13 compressors, reflecting variation in occupant schedules and other uncontrolled factors in occupied buildings. Median weather-normalized energy savings per compressor were 21%. Staging ceiling fans and air conditioning provided comfort across a wider temperature range, using less energy, than air conditioning alone.

Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods

Residential buildings consume a large amount of energy in operation, which can be reduced by planning energy-efficient design and operation strategies. Understanding and quantifying the impact of key driving factors to the residential building energy end-use is essential for promoting more energy-efficient building design and operation schemes. Although the climate, building, and occupant-related features have been proven as the key driving factors to residential building energy end-use, their impacts are rarely compared and quantified simultaneously. This study conducts the first attempt in combining the machine learning method with Monte Carlo method to quantify the impacts of these key driving factors simultaneously. Data collected from the Residential Energy Consumption Survey 2015 of the U.S. was investigated in this study. The results indicate that the total energy end-use has positive correlations with the total building area, rooms’ numbers, windows’ numbers, indoor heating temperature setpoint and the occupants’ age; and has a negative correlation with the cooling temperature setpoint; the impacts of heating degree days and cooling degree days are nonlinear and complicated; the impact of the level of insulation is nuanced and offset by the harsh climate. The results of this study contribute good references to policymakers and architects for the synthesis of energy-efficient residential building design and operation; guidelines can be developed for the future survey on residential building energy for relevant and precise data collection to improve the building energy modelling.

Multi-objective design optimization on building integrated photovoltaic with Trombe wall and phase change material based on life cycle cost and thermal comfort

This study performed design optimization on a building with integrated photovoltaic, Trombe wall and phase change material based on building life cycle cost (LCC) and indoor thermal comfort. A total of thirty-four design variables were considered as the optimization variables. The optimization process consists of four steps. Firstly, Monte Carlo Method was used to generate a set of design samples, and the LCC and indoor discomfort degree hour were obtained through computer simulation. Secondly, stepwise linear regression (SLR) and artificial neural network (ANN) models were developed as the prediction models. ANN models exhibited better prediction performance with errors of less than 6%. Thirdly, six optimization algorithms were selected to couple with the ANN models for design optimization and the Strength Pareto Evolutionary Algorithm II (SPEA II) presented the best performance. Finally, optimization with different groups of design variables were conducted by using the SPEA II algorithm. Compared with the reference building, the optimal solutions reduce life cycle cost up to 45.51% and improve thermal comfort level up to 43.81%. The recommended heating and cooling temperature setpoint is around 18 °C and 24 °C, respectively. The PCM type convergence value is BioPCM M182/Q21, and the east and west window-to-wall ratios are around 10%.

An archetype-in-neighbourhood framework for modelling cooling energy demand of a city's housing stock

As hot days are getting hotter and more frequent, urban dwelling is expected to increase cooling energy use in current and future climate. The applicability of dynamic building simulation in estimating cooling loads of a city's housing stock can be limited due to lack of fine-grained on-site current and future weather inputs. For predicative modelling of residential cooling energy demand to aid a city's energy supply planning resilient to excessive heat conditions, it requires cooling energy demand projection based on a relational account of (1) the thermal-environmental interaction between housing stocks and urban microclimate conditions, (2) the city dwellers' cooling energy use behaviour, and (3) the city's climate projections. In this paper, we introduce an ‘archetype-in-neighbourhood’ framework to meet these requirements. Combining empirical urban data modelling and EngeryPlus model calibration, this framework was developed to obtain statistically a maximal cooling energy demand model of a city's housing stock during yearly hottest periods. We applied the framework to multiple datasets selected from Seoul's open urban data sources for the period of 2014–2017 (2014 being the earliest year of data availability, 2017 being the end of the study period), including metered electricity use data of 659 apartment buildings (51,351 households) sampled from 18 city districts. The results show that maximal month cooling energy demand (MMCD, kWh/m2) of Seoul's housing stock can be expressed as a regression function of two determinants: (1) the city's average outdoor temperature during the hottest month period (Tex, °C), and (2) estimated indoor cooling temperature set-point (Tin, °C) of the city’ housing stock during the same period. Through a k-fold (k = 4) validation, the current regression model (2014–17) was evaluated to have an overall coefficient of determination R2=0.969. Assuming no housing stock renovation, we applied the model to generate scenarios of maximal month cooling demand in future years according to some of the highest summer temperatures projected for Seoul (RCP8.5 2045, RCP4.5 2047, MM5 2071–2100). We conclude this paper with a brief discussion of the implication for cooling energy supply planning and further work to extend the applicability of this new framework to housing stock adaptation planning and design.

Thermal comfort evaluation in campus classrooms during room temperature adjustment corresponding to demand response

Most thermal comfort standards and guidelines currently in use do not consider occupants' adaptive capabilities associated with real-world situations when predicting occupant thermal comfort in mechanically conditioned buildings, although the adaptive approach is commonly applied to naturally ventilated buildings. These standards are generally based on results derived from experiments conducted in climate-controlled chambers. In some cases, this can lead to overcooling of buildings while still not satisfying most of occupants. One common method to reduce peak electricity demand is via temporarily increasing cooling temperature setpoint during peak hours. However, the potential negative impacts on occupant thermal comfort and wellbeing calls for further study on this. This paper describes a study conducted on a university campus in the United States that investigated occupants' thermal sensation, acceptability, and preferences corresponding to increased cooling temperature setpoint in parts of the building that are temporarily occupied. The results revealed a potential for at least temporarily increasing cooling temperature setpoint (at least 2 °C) across this campus without impairing occupant thermal comfort. For operative temperatures between 22 and 24.5 °C, the average Actual Mean Vote (AMV) for the class sections remained in the ASHRAE comfort range and the self-reported thermal acceptability was above 80%. Occupants’ thermal acceptability dropped to less than 80% when the temperature was increased to more than 24.5 °C, and the AMV values increased to more than 0.5 (on ASHRAE 7-point scale). The percentage of occupants who were involved in some sort of adaptive behavior did not considerably change with room temperature.

Cooling energy reduction effect of parallel double-window system operation in residential buildings in South Korea

This paper aims to derive the operational modes of a parallel double-window system that reduces cooling energy consumption and satisfies indoor comfort through natural ventilation. The parallel double-window system examined in this paper is a window system that could control indoor draft distribution and adjust the size of the opening depending on indoor and outdoor conditions. The system can be used in five ways (all close, out-open + in close, out-open+in open (tilt), out-open+in open (turn) and all open). This work verified the energy savings and indoor comfort of the existing mode experimentally, which were originally derived based on simple calculations at the time when the parallel double-window system was developed. A new operation mode, Alt 1, was derived, which addressed problems of the existing mode. In addition, in this work, the operation mode Alt 2 was derived, which simplified Alt 1 so that the actual occupant can use the system easily. By measuring these three operation modes and comparing the results with those of energy plus simulations, the work derived the amount of cooling energy savings and the level of indoor comfort through the use of an appropriate operation mode during inter-seasonal periods. Compared to when the natural ventilation operation mode was not used, cooling energy consumption was reduced by 60% when the operation mode was in use. The cooling temperature set point could have a significant impact on cooling energy consumption.

THE IMPACT OF OCCUPANT BEHAVIOUR ON RESIDENTIAL GREENHOUSE GAS EMISSIONS REDUCTION

In 2011–12, greenhouse gas emissions from the Australian residential sector were 101.6 Mt and are expected to grow by 38% by 2050. In order to reduce these emissions, much emphasis has been placed on increasing the energy efficiency of buildings and appliances. Occupant behaviour, however, is probably the single most significant factor which determines energy use and emissions. This paper describes research undertaken to rank the most common occupant behaviours, based upon their impact on greenhouse gas emissions associated with residential energy use, in an architect-designed house in Australia. The occupant behaviours investigated were changing: the heating and cooling temperature set points, window openings, external blind use and lighting use. Simulations were carried out using Primero and EnergyPlus software. Based on the simulation results of greenhouse gas emissions, the following ranking of overall influence (from most influential to the least) has been determined: external blind use was one of the most effective measures to reduce emissions. Cooling set point temperature was similarly important with the magnitude of impact depending on the set point e.g. a 2°C increase had an impact comparable to the use of external blinds. The impact of the heating set point temperature was also dependent on the set point and overall slightly lower compared to the cooling set point temperature. Lighting use was the least influential parameter in the context of this study.

Productivity metrics in dynamic LCA for whole buildings: Using a post-occupancy evaluation of energy and indoor environmental quality tradeoffs

The IEQ + DLCA framework, which integrates indoor environmental quality (IEQ) and dynamic life cycle assessment (DLCA) at the whole-building level, was revised and expanded to consider non-chemical health impacts and productivity/performance impacts. The complete framework was evaluated for a case study of a LEED gold rated university building, supplemented with a post-occupancy evaluation (POE) designed to elicit qualitative feedback on IEQ and productivity impacts specific to the building. Most non-chemical health impacts (e.g. sick building syndrome (SBS), asthma) were not able to be included in the framework, due to potential overlaps with chemical-specific impacts already included in LCA. However, productivity impacts were able to be included in the framework for the purpose of comparing different design and operational choices for a given building. Occupants were also asked to evaluate the anticipated effects on their individual productivity due to IEQ changes associated with hypothetical energy-saving strategies. Results of the POE showed occupants were generally satisfied with IEQ, and considered that increasing summer cooling temperature set points would enhance productivity. Energy savings were calculated using an empirical energy and IEQ model calibrated with sensed data from the building. Evaluation of the full IEQ + DLCA framework suggested potentially significant energy savings potential and possible productivity enhancement, but indicated tradeoffs between internal chemical impact categories related to building ventilation, indoor pollutant generation and outdoor pollutant intake. The tradeoffs and overlaps between internal chemical impact categories themselves, as well as between chemical and non-chemical impact categories such as (SBS) are a challenge for LCA of whole buildings.

Data center optimization using PID regulation in CFD simulations

Data center electric energy consumption becomes a major issue in HVAC (Heating Ventilating and Air Conditioning) design. In the 2011 guidelines, the ASHRAE (American Society of Heating Refrigerating and Air conditioning engineer) introduces several allowable working envelopes based on temperature and hygrometry. These envelopes encourage the designers to raise cooling air temperature, increasing the COP (Coefficient Of Performance) of the chiller and decreasing its energy consumption. But server's thermal behavior of recent blade server evolves. Their heat rejection is now mainly driven by leakage phenomenon at the CPU level and a FSCA (Fan Speed Control Algorithm) modulate their air flow rate in function of their inside temperature. In this paper we introduce a new way to model this kind of server in a Data center CFD (Computational Fluid Dynamic) code. It includes air speed control with a PID (Proportional Integral Derivative) algorithm, CPU leakage simulation and overall material electric consumption. We will then test this model with different cooling air temperature corresponding to the ASHRAE recommended range. We will try to select the appropriate cooling temperature set point to reach the best compromise between the chiller and the server energy consumption.

건물의 외주부 존에 대한 동적 부하모델 이용 피크냉방부하 저감효과 분석

In this paper, inverse building modeling was applied to building perimeter zones which have different window orientation. Two test zones of east-facing and west-facing zones in ERS(Energy Resource Station) building, which is representative of small commercial building, was used to test performance of cooling load calculation and peak cooling load reduction. The dynamic thermal load model for the east and west zone was validated using measured data for the zones and then it was used to investigate the effect of peak cooling load reduction by adjustment of indoor cooling temperature set points during on-peak time period. For the east zone, the peak load can be reduced to about 60% of the peak load for conventional control even without any precooling. For the west zone, PLR is nearly independent of the start of the on-peak period until a start time of 1pm. Furthermore, PLR has a small dependence on the precooling duration. Without any precooling, the peak cooling load can be reduced to about 35% of the peak load associated with conventional control.

An investigation of optimal control of passive building thermal storage with real time pricing

The cost savings potential of optimal passive thermal storage control were examined for day-ahead real time electricity rate structures. The operational strategies of three office building models were optimized in four US cities (Chicago, New York, Houston and Los Angeles) using price and weather data for the summer of 2008. Optimization of building thermal mass was conducted using a predictive optimal controller to define supervisory strategies in terms of building global cooling temperature setpoints. A global minimization algorithm determined optimal setpoint trajectories for each day divided into four distinct time periods. Cost savings were found to range from 0 to 14% depending on the building, climate, and characteristics of the rate signal. The best cost savings occurred for price spikes or cool nighttime temperatures. Moreover, it was found that low internal gains favoured a more flexible precooling strategy, while high internal gains coupled with low thermal mass resulted in poor precooling performance.