Cooling Set Point Research Articles

Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations.

Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict simulation results. To be useful for such purpose, it should be possible to quickly train precise surrogate models from a small number of simulation results (10-100) obtained from appropriately sampled points in the desired part of the design space. Two sampling methods and two machine learning models are compared here. Latin hypercube sampling (LHS), widely accepted in building energy community, is compared to an exploratory Monte Carlo-based sequential design method mc-intersite-proj-th (MIPT). Artificial neural networks (ANN), also widely accepted in building energy community, are compared to gradient-boosted tree ensembles (XGBoost), model of choice in many machine learning competitions. In order to get a better understanding of the behaviour of these two sampling methods and two machine learning models, we compare their predictions against a large set of generated synthetic data. For this purpose, a simple case study of an office cell model with a single window and a fixed overhang, whose main input parameters are overhang depth and height, while climate type, presence of obstacles, orientation and heating and cooling set points are additional input parameters, was extensively simulated with EnergyPlus, to form a large underlying dataset of 729,000 simulation results. Expendable local surrogate models for predicting simulated heating, cooling and lighting loads and equivalent primary energy needs of the office cell were trained using both LHS and MIPT and both ANN and XGBoost for several main hyperparameter choices. Results show that XGBoost models are more precise than ANN models, and that for both machine learning models, the use of MIPT sampling leads to more precise surrogates than LHS.

Implementation and validation of optimal start control strategy for air conditioners and heat pumps

Commercial buildings are responsible for approximately 20 % of the total energy consumption and greenhouse gas emissions in the United States. Over 85 % of these buildings lack building automation systems, and many are small (<50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, several operational deficiencies lead to excess energy consumption. Studies have shown that managing the RTUs’ heating and cooling set points, schedules, setbacks, and optimal start can result in a 20 % to 25 % reduction in electricity consumption in small commercial buildings. These buildings typically use fixed schedules to start the RTUs 60 to 120 min before occupancy begins, which results in excess energy consumption. This paper presents research that demonstrates and evaluates the performance of four optimal start methods, which utilize data-based modeling as a key element in facilitating adaptive control in response to time-varying inputs while requiring minimal sensor inputs. The evaluation found energy savings in two commercial buildings equipped with RTUs by periodically alternating four different optimal start models during the cooling and heating season. The resulting energy savings are positive for all models and range from 2 to 5 kWh/day/unit. The units on the east side of the building showed higher savings, while interior units showed greater variability in savings due to the differences in capacities and room sizes. Savings were considerably greater during the heating season compared to the cooling season. The performance of all four models on Mondays was poor; models suggested a shorter optimal start time, which resulted in relatively larger errors. The future work will look at using a different model for the days after weekends and holidays.

Implications of latent and sensible building energy loads using natural ventilation

Building decarbonization requires balancing energy efficiency with occupant comfort. Sensible cooling and latent loads, traditionally addressed by air conditioning, are significant contributors to energy use. This study explores an innovative approach to utilize natural ventilation through windows when outdoor conditions are favorable. The novelty of this work lies in its comprehensive consideration of sensible and latent loads, including both humidification and dehumidification, within the context of natural ventilation. When in cooling mode, if the indoor temperature rises above the cooling set point and the outdoor temperature is lower than the indoor temperature, the windows will automatically open to enhance natural ventilation, provided the specific humidity conditions for indoor, outdoor, and set point are met. Simulations using the Vertical City Weather Generator (VCWG v1.4.7) software investigate the energy savings potential in Toronto, Vancouver, and Phoenix for 2020. Results indicate potential for notable annual total cooling load savings of 5–15 kW-hr m−2 Year−1. Additionally, further analyses examine the impact of building air-tightness, envelope thermal resistance, and climate zone on the effectiveness of natural ventilation to reduce the sensible cooling, latent, and total cooling loads.

Sustainable construction of green school building using energy simulation analysis and modeling

The aim of this research is to manage energy efficiency in the design of green school buildings in order to create an optimal educational environment and sustainable architecture. The research method is quantitative and a simulation calculation. For this purpose, Ecotect 2021 software is used for modeling and Revit 2021 and DesignBuilder software for analyzing the results. The case study of this research is the schools built by the School Renovation Organization of East Azarbaijan Province, Tabriz City, based on the cold climate type plan. On this basis, a school designed and implemented by the School Renovation Organization of East Azarbaijan Province was selected based on a non-random purposive sample. The model is implemented in Revit simulation software and the energy analysis is then performed with DesignBuilder software using sustainable materials and renewable energy. The case study building includes appropriate materials in the walls and windows, standard thermal insulation, solar panels on the ceiling and walls, standard ventilation and wind turbines for lighting. The model is analyzed with the DesignBuilder simulator software and then the design is created with the most suitable placement angle based on the optimal energy consumption. The simulation results show that the optimal orientation of the building with the optimal overlap of the shadow finder band and the diagram obtained is 10° to the southeast. The final result of the analysis of the optimal energy consumption of the building at a cooling set point temperature of 24°–26° based on the window to wall ratio from October to February shows a value of 84900 kw/h to 88800 kw/h. According to the optimal design of the building and the results of the energy analysis, the cost of building a school based on the cost of services and infrastructure per gross internal floor area is £959,815, which is the optimal cost of implementing a green school. The results have shown that all selected components in the green schools in the cold regions of Iran to achieve the main goal of using as much solar energy as possible, compensating for unfavorable climatic conditions and creating comfortable conditions for students through the implementation of sustainable architecture.

Estimating the energy consumption for residential buildings in semiarid and arid desert climate using artificial intelligence

This research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation, envelope construction materials properties, window-to-wall ratio, heating and cooling set points, and glass properties. The performance of the developed models was evaluated in terms of the RMS, R2, and MAPE. The results showed that the EPR model is the most accurate and practical model with an error percent of 2%. Additionally, the energy consumption was found to be mainly governed by three factors which dominate 87% of the impact; which are building size, Solar Heating Glass Coefficient (SHGC), and the target inside temperature in summer.

Energy Performance and Life-Cycle Analysis of Building Retrofits: A Case Study in Abu Dhabi

Highly developed nations worldwide encounter a notable energy demand as their main obstacle. Furthermore, the building sector plays a significant role in contributing to carbon emissions and climate change. In the UAE, buildings consume the largest portion of energy due to the improper selection of design parameters during the building&amp;apos;s design phase, which are specifically tailored for the remarkably hot climate in the country. As a result, various studies, initiatives, and policies are focused on enhancing the energy efficiency of buildings. Additionally, retrofitting existing buildings has emerged as a crucial approach to achieving energy efficiency, resulting in several benefits such as reduced costs for operation and maintenance. This research performed an analysis of a commercial building in Abu Dhabi using DesignBuilder, based on energy modeling and simulation. Five main retrofits were examined, accompanied by a cost analysis to determine the most appropriate retrofit for future investments. The results demonstrate that increasing the cooling set point temperature by 4 degrees led to a 19.53% decrease in the annual cooling load. Additionally, retrofitting the chiller resulted in a 16.11% reduction in the annual cooling load, whereas wall insulation had the least impact as a retrofit. It was observed that improving the chiller&amp;apos;s coefficient of performance (COP) offered significant advantages, with a payback period of around 5 years, making it the most favorable retrofit for investment. However, the glazing and wall insulation retrofits were considered less beneficial due to their high initial costs and long payback periods.

Robust multi-objective building design optimization approach using NSGA-II algorithm considering weather and occupant uncertainties

A novel methodology is presented to evaluate how real-world operational uncertainties impact multi-objective building design optimization process. NSGA-II algorithm is applied across nine distinct operational scenarios, considering diverse weather datasets and energy usage patterns to optimize energy efficiency and thermal comfort in a residential building within India's hot and dry climate. The analysis considers 382,400 possible design options for each scenario. Using (10.5–12.5) % of model evaluations, NSGA-II significantly reduces Annual Cooling Energy Demand (ACED; 46.7%–58.7%) and Cooling Set-point Unmet Hours (CSUH; 70.92%–86.93%) to identify nine Pareto–Optimal solution sets. The final robust optimal design is confirmed by selecting the most repeated parameter settings, representing the highest robustness. Low thermal transmittance walls, double-glazed windows with a 10% window-to-wall ratio (WWR), deep overhangs, and a low solar heat gain coefficient are recognized as robust optimal building components. However, Air Conditioner (AC) sizing lacks robustness in minimizing energy demand across scenarios, requiring case-specific evaluation due to its sensitivity to weather and operational variations. Further, PAWN sensitivity analysis highlighted the substantial impact of WWR (KSWWR ∼ (0.46–0.66)) and wall type (KSWall ∼ (0.31–0.46)) on ACED and CSUH in chosen climate. AC operation is more important for predicting CSUH (KSAC ∼ 0.35) than ACED (KSAC ∼ 0.10).

Coupling PCM wallboard utilization with night Ventilation: Energy efficiency and overheating risk in office buildings under climate change impact

The rising population and increasing thermal comfort expectations are expected to exacerbate the already high HVAC use in offices, which unavoidably places a strain on energy supply systems. Storing thermal energy by incorporating phase change materials (PCMs) is an effective method for improving the buildings’ energy efficiency. Understanding how a PCM wallboard responds to climate change (CC) is crucial to maintaining its viability and effectiveness in achieving energy efficiency and sustainability goals during a building’s lifetime. This study presents a methodological framework to assess the performance of a building utilizing PCM wallboards in terms of cooling/heating energy demand (and corresponding GWP and operation cost) and occupant thermal comfort considering the changing climate. As a case study, a hypothetical office building located in a cold semi-arid climate with high diurnal fluctuations was selected, and the performance of offices orienting in different directions was evaluated when PCM wallboards with different melting ranges were utilized. PCM19 with a melting range close to heating set point reduced the heating demand by 5.0–7.4% for 2020 and 7.8–9.2% for 2050; while PCM25 with a melting range close to cooling set point yielded cooling reductions of 1.9–4.3% for 2020 and 0.7–2.4% 2050. However, PCM19 and PCM25 were ineffective in cooling and heating, respectively, as they were not compatible with the chosen set points. Coupling PCM25 with night-ventilation (NV) significantly enhanced the cooling savings (NV: 23.2–25.7% for 2020; 13.9–15.7% for 2050; NV + PCM25: 29.4–30.6% for 2020; 16.6–17.5% for 2050); though, the effectiveness of NV was impaired with rising night temperatures due to CC. Hourly energy analysis demonstrated varying performance of wallboards based on the time of day, suggesting potential benefits in supply–demand dynamics and time-of-use energy pricing. In naturally-ventilated scenarios, PCM utilization was ineffective in reducing indoor overheating risk, even when night-ventilation was introduced. Results highlight that to effectively mitigate indoor overheating risk in office buildings, PCM wallboards should be coupled with air-conditioning and night-ventilation. Furthermore, for a comprehensive evaluation of the energy/GWP/cost saving potential of a PCM wallboard during a building's lifetime, not only the current but also the projected local climate should be considered given the shifting energy demand towards cooling due to climate change. Overall, this study provides valuable insights into effective PCM wallboard utilization and lays the groundwork for enhancing the resilience of built environments in the face of climate change challenges.

Analysis of the energy performance strategies in a historical building used as a music school

Energy consumption in public education buildings depends on use and occupancy. To improve energy performance, energy audits are essential to identify specific solutions for each building. In this study, we conducted an energy audit of a historical building used as a public education centre. We collected gas and electricity bills and recorded indoor temperatures for over a year to determine heating and cooling set points and the schedules. Our analysis showed that 47.42% of the electricity bills were unused. To reduce energy demand and improve thermal comfort, we both developed and validated a Building Energy Modelling (BEM) approach using TRNSYS18 and weather data during year 2021. The BEM model allowed us to propose efficient measures to meet the Standard Passive requirements. Our results demonstrate the effectiveness of energy audits and BEM modelling in reducing energy consumption in public education buildings.

Prediction of heating and cooling loads based on light gradient boosting machine algorithms

Machine learning models have been widely used to study the prediction of heating and cooling loads in residential buildings. However, most of these methods use the default hyperparameters, resulting in inaccurate prediction accuracy. In this work, based on hyperparametric optimization algorithms of random search (Random), grid search (Grid), covariance matrix adaptive evolution strategy (CMA-ES), and tree-structured parzen estimator (TPE), were combined with the light gradient boosting machine (LightGBM) model, to construct four hybrid models (Random-LightGBM, Grid-LightGBM, CMA-ES-LightGBM and TPE-LightGBM) for improved prediction accuracy of heating and cooling loads. The LightGBM model was trained using a dataset consisting of building features, cooling set points, and occupant behavior parameters. Feature selection was performed by a random forest-based feature selection method, which determines the input features of the load prediction model. The TPE- LightGBM model achieved the best prediction accuracy among all proposed models with a root mean square error (RMSE) of 0.2714, mean absolute error (MAE) of 0.1416, coefficient of determination (R2) of 0.9981, and mean absolute percentage error (MAPE) of 0.4699% for heating load prediction, and RMSE of 0.1901, MAE of 0.1394, R2 of 0.9924, and MAPE of 2.3509% for cooling load prediction. The proposed TPE-LightGBM model provides an efficient strategy for predicting heating and cooling loads, which can provide better energy efficiency measures at the early design stages of residential buildings.

Is 24.9 °C Too Hot to Think? A Call to Raise Temperature Setpoints in Australian Offices

The current +−0.5 PMV (Predicted Mean Vote) targets adopted by NABERS (National Australian Built Environment Rating System) is the practical range deemed acceptable for 90% acceptability for commercial buildings in Australia, however thermal comfort satisfaction scores measured in office buildings still show high percentages of dissatisfied occupants. This paper aims to demonstrate the potential of curbing energy consumption from commercial buildings in Australia by increasing summer temperature set-points. A 10-year NABERS dataset, along with objective and subjective thermal comfort and air quality data from NABERS-certified offices are investigated in this study. Furthermore, different simulation scenarios are tested to investigate the discomfort hours and energy consumption for various summer temperature setpoints. Result analysis shows that occupants’ satisfaction in NABERS-certified buildings was not within the 90% satisfaction, with being too cold/hot as the main source of dissatisfaction. Objective measurements also showed temperature was out of recommended range for several datapoints. Simulation results indicate that, within the average range of 21–24.9 °C, there is not a significant difference in discomfort hours that could drive the selection of one temperature set-point over the other. Challenging the current practices, results suggest that a cooling set point temperature on the upper limit of the range indicated by the Australian standard AS 1837–1976 may minimize the energy consumption without significantly increasing discomfort, or even increasing the perceived satisfaction with the indoor environment.

Unlocking demand response in commercial buildings: Empirical response of commercial buildings to daily cooling set point adjustments

Buildings represent over half of global electricity demand. Cooling buildings already accounts for over 9% of global electricity demand and is expected to grow rapidly due to climate-change induced hot-spells and increasing prosperity in developing economies. In the US, commercial buildings represent 35% of nationwide electricity consumption. Increased electricity demand for cooling services will challenge already stressed power grids, particularly during times of peak demand. This work explores the flexibility and demand response potential of large Heating, Ventilation and Air Conditioning (HVAC) systems based on an extensive set of measurements from six commercial buildings in a Warm-summer Mediterranean climate. Over a three-month summer period, zone-level temperature set points were adjusted daily in six commercial buildings to determine the effect on chilled water and electricity loads, as well as on zone-level temperatures. External weather conditions were measured continuously during the testing period. The experimental data that were collected are published with this article. These experiments confirmed the potential to provide flexibility by reducing energy demands based on modest zone-level temperature set point adjustments. A two-degree Fahrenheit increase of the cooling set point resulted in a 13–28% reduction in daily building-level cooling loads on average for four office buildings and 3–4% for two laboratory buildings. The impact on electric loads was less than 2% (excluding for cooling water but including for ventilation). Zone-level temperature increases were measurable but temperatures remained within the target ranges. By collecting 385 experiment-days of experiment data, we were able to parameterize statistical models for the response of the buildings. These models provide statistical guarantees on the reliability of thermal demand response. This work provides a blueprint for constructing building and zone-level energy-response functions and highlights the value of testing buildings repeatedly and across a range of weather conditions. Providing statistical performance guarantees will be critical for widespread adoption of demand response technology to provide the flexibility needed to meet peak electricity demands. Combined with thermal storage, the daily flexibility studied here would also unlock daily and sub-daily electrical flexibility, and can also be integrated with sub-daily flexibility from building-level electrical loads.

Comparative energy performance evaluation and uncertainty analysis of two building archetype development methodologies: A case study of high-rise residential buildings in Qatar

Building Archetypes (BAs) are often used in modeling urban-scale building energy consumption profiles to represent the energy consumption behavior of typical buildings within the modeled urban environment. One current approach to the development of BAs involves the creation of Building Energy Models (BEMs) using surveyed data coupled with codes, standards, or engineering experience. Another approach is the modification of existing standardized BEMs using available data to produce a suitable BA for the representation of the building stock. The adoption of either of these methods has been treated as an axiom or a step that did not need much consideration. However, the results produced by BEMs created according to different methodologies present several differences. The discrepancy in the produced results is shown not only in the results of the BEMs developed according to different methodologies but also in the degree to which these results vary when changing input values. Limited studies analyzed the sensitivity of these models to uncertain inputs, even though it is essential for the development of BA libraries. This paper comparatively investigates the Region-Specific (RS) and Department of Energy (DOE)-based BEM creation methods for BA library development. The models are developed by following the two methodologies, and their predictions are compared to the metered data from a high-rise residential building in the Marina district of Lusail City, Qatar. A Global Sensitivity Analysis (GSA) method is used to identify the most influential parameters contributing to the energy consumption variation of these models. Further, a parametric simulation campaign is conducted by varying the inputs determined earlier to quantify the BEMs’ output variability. According to the results, the baseline RS BEM and DOE BEM yield an Energy Use Intensity (EUI) of 211.03 kWh/m2 and 137.9 kWh/m2, respectively. The deviation of these values from the metered EUI of 185,37 kWh/m2 indicates that the RS BEM development methodology is preferable to the DOE BEM for the BA development. Based on the GSA results, regardless of the BEM development methodology used, the most important parameters contributing to output variation are the Cooling Setpoint, the Outdoor Air Requirements, the Equipment Power Density, and the Lighting Power Density. Further, the RS BEM’s parametric simulations produce a mean cooling EUI of 324.83 kWh/m2, closer to the literature benchmarks than the mean cooling EUI of DOE BEM, 341.62 kWh/m2. The results obtained from the baseline model and the uncertainty analysis support the conclusion that a Region-Specific Model developed from the ground up serves to better represent high-rise residential buildings in regions with extreme climate conditions. The impact this work achieves is providing decision-making support for urban scale building energy modelers for future building stock representation efforts in such areas while also providing an expected uncertainty range for the developed high-rise residential archetypes.

Thermal adaptation of different set point temperature modes and energy saving potential in split air-conditioned office buildings during summer

The Chinese government issued a regulation that the cooling set point temperature (SPT) in office buildings cannot be lower than 26 °C. Therefore, SPTs were divided into different modes: SPT≥26 °C and SPT<26 °C. To study the human thermal adaptation of different SPT modes and determine the comfort and energy saving of the policy, a field test and subjective questionnaires were conducted in 21 split air-conditioned (SAC) office buildings during the warm season in the cold zone of China. We measured the environmental parameters and subjects' skin temperatures, and the subjective thermal responses were also investigated. The results showed that 45% of the air conditioner SPTs were less than 26 °C. In the high SPT mode, people's physiological adaptability to the warm environment was stronger, and subjects were more active in reducing clothes and increasing air velocity to improve thermal discomfort. Because the two modes had similar thermal histories and the same perceptual control, in the same SET ranges, no significant differences were found in thermal sensation, acceptability and comfort between the two SPT modes. The effects of long-term indoor thermal history on human thermal adaptation varied with the thermal exposure type, mode and intensity. From the analysis of thermal adaptation, the rationality of the policy was fully proved, and it was determined that SAC office buildings could save 26.4–35.2% of power energy consumption by reasonably increasing SPT in the cold zone. The results can enrich the thermal adaptation theory and provide a theoretical basis for the government's energy-saving policies in SAC office buildings.

Utilizing innovative proportional-integral-derivative controllers to reduce solar air conditioning system energy demand

In this study, a new proportional-integral-derivative controllers (PID) system was designed to analyze chiller energy demand (CED) in accordance with current criteria for lowering energy consumption in buildings (TED). By defining parameters of cooling setpoint (CSP) and cooling set back (CSB) schedule, the activity of the air conditioning system along with mechanical ventilation was adjusted well-ordered by the PID controllers. In the first step, it was found that a one-degree change in the CSP parameter had a much superior effect than a three-degree change in CSB. In this study, the heat recovery potential in Riyadh was evaluated and it was found that adding this system with a yield of 0.5 to 0.8, diminished TED in the range of 33.4 to 50%. With the development of a system of PID controllers, the economizer decreased CED by 1 kWh/m2 during CSB. Although the heat recovery system was much more successful than the economizer, to benefit the advantage of both devices simultaneously, a suitable PID system was designed to activate the economizer in CSB time and heat recovery in CSP. Due to the proper combination of these two devices, TED was reduced by 54.3%.

Investigating Optimum Cooling Set Point Temperature and Air Velocity for Thermal Comfort and Energy Conservation in Mixed-Mode Buildings in India

In warm and hot climates, ceiling fans and/or air conditioners (ACs) are used to maintain thermal comfort. Ceiling fans provide air movement near the skin, which enhances the evaporation of sweat, reduces heat stress, and enhances thermal comfort. This is also called the cooling effect. However, AC usage behaviour and the effects of elevated air speed through the use of ceiling fans on indoor operative temperature during AC usage are not widely studied. This study investigated the optimum AC (cooling) set point temperature and air velocity necessary for maintaining thermal comfort while achieving energy conservation, in mixed-mode buildings in India, through field studies by using used custom-built Internet of Things (IOT) devices. In the current study, the results indicate a 79% probability that comfort conditions can be maintained by achieving a temperature drop of 3K. If this drop can be achieved, as much as possible, through passive measures, the duration of AC operation and its energy consumption are reduced, at least by 67.5 and 58.4%, respectively. During the air-conditioned period, there is a possibility that the cooing effect is reduced because of increase in operative temperature due to ceiling fan operation. Therefore, the optimum solution is to maintain the highest AC set point and minimum fan speed setting that are acceptable.

Comfort-based control for mixed-mode buildings

International thermal comfort standards are applicable for the design and operation of either mechanically cooled or naturally cooled buildings and limited guidance is given for mixed-mode buildings. In this study, a control framework for mixed-mode buildings was defined based on the adaptive comfort model and PMV-PPD method. The proposed framework was tested using a simulation-based analysis of a central module of an office building. The results were compared with a mechanically cooled building. The objective was to characterize how to control mixed-mode buildings optimally, regarding both energy use and thermal comfort. Five locations were considered: Copenhagen - DK, Edinburgh - UK, Palermo - IT, Tokyo - JPN, and Zurich - CH. The mixed-mode control strategy had a primary energy use between 12 and 51 % lower than the mechanically cooled case. In this context, using the upper limit of the adaptive comfort zone as cooling set point rather than the upper limit of the PMV-based comfort zone showed nearly 20 % more energy savings and fewer switchovers between operation modes. Night cooling led to lower operative temperatures and fewer switchovers between operation modes as well as additional energy savings of 10 % only in Palermo. The results show that a mixed-mode building operated based on the adaptive comfort criteria can have a large reduction of energy use without compromising thermal comfort or indoor air quality, compared to a mechanically cooled building.

BIM-based techno-economic assessment of energy retrofitting residential buildings in hot humid climate

Buildings in hot humid climates are energy intensive to operate. Energy consumption in Saudi Arabia is almost three times higher than the global average, and one of the major contributors to that is the residential sector. Increasing environmental and economic concerns, in the form of Saudi Vision 2030, mean that the existing unsustainable residential building stock has to be energy retrofitted. This study examines the techno-economic feasibility of retrofitting existing homes in Eastern Province, Saudi Arabia. A Building Information Modelling (BIM)-based retrofit framework has been adopted and investigated on two case studies. Eight Energy Efficiency Measures (EEMs) have been implemented including increasing cooling set point temperature, using energy efficient appliances, replacing conventional lights with more efficient lights, applying window shading, improving glazing type, improving air tightness, using more efficient air conditioning system, and adding envelope insulation. A three-level energy retrofit plan is proposed. Results indicate that annual energy consumption in a villa is reduced by 13.79%, 19.27% and 56.9%, and in the apartment building by 22.84%, 28.85% and 58.5% through a level 1, 2 and 3 retrofit respectively. Compound Payback Period (CPP) was computed to assess the economic viability. For the villa, investing in a level 1, 2 and 3 retrofit will pay back in 0.92, 8.37 and 25.15 years respectively, while for the apartment building, the payback period is 0.60, 11.28 and 24.60 years respectively. Thus, energy retrofitting of existing homes has the potential to significantly reduce energy consumption, however, it is economically not viable in the existing scenario. Electricity tariffs need to be further increased and coupled with incentive programs to render deep energy retrofits feasible. Furthermore, the investigated BIM-based approach can be adopted to investigate energy retrofitting in other climate zones of Saudi Arabia and other countries in the region, and eventually be adopted to efficiently effectuate the colossal task of retrofitting the whole existing residential building stocks of these countries.

Model predictive control of building energy systems with thermal energy storage in response to occupancy variations and time-variant electricity prices

Developing effective operational strategies for commercial buildings is a high priority as the global community seeks to reduce energy usage and greenhouse gas emissions. This study aims to validate the feasibility of a model predictive control (MPC) strategy for commercial building in response to occupancy variations and time-variant electricity prices in comparison to a conventional rule-based control (RBC) strategy. The building energy system included an air-cooled chiller, stratified chilled water thermal energy storage, two fan coil units, three heat exchangers, and five pumps. The optimal operations of the chiller and storage system were determined with the goal of minimizing the operating cost while maintaining the zone temperature at a cooling set point temperature during cooling operation hours. Artificial neural network was utilized as prediction models and metaheuristics algorithm was employed as optimization solver to construct a reliable and computationally manageable MPC controller. The simulation was performed for four days during the cooling season with a confirmed optimal prediction time horizon of 24 h and a control timestep of 1 h intervals. In conclusion, MPC reduced the total operating cost by 3.4% compared to the RBC, which prioritized the storage system operation to manage the thermal load.

Minimizing delivered energy and life cycle cost using Graphical script: An office building retrofitting case

Selecting the most cost-effective retrofit interventions to achieve a significant reduction of energy use and CO2 emissions in the building sectors is challenging, because a large number of possible retrofitting options should be analyzed. To remedy this and simplify the decision-making process, optimization may be adopted. This study developed an iterative optimization process by coupling a dynamic energy simulation software, IDA-ICE, and a generic optimization engine, GenOpt, through the Graphical Script module. This optimization process was applied to an office building located in the Nordic climate. Two scenarios were considered. In the first scenario, the optimal designs were achieved by minimizing the life cycle cost of retrofitting measures over a span of 60 years, while the building energy use for space heating and cooling were the constraints to satisfy the Norwegian passive house standard level. In the second scenario, the delivered energy to the building was minimized and the life cycle cost of retrofitting was limited to a predefined value. Two different space heating systems were used, radiator space heating and all-air systems. The optimization parameters included building envelope elements and heating and cooling set points (in the case of all-air system). The results showed that the specific life cycle cost could be reduced up to 11%, while the energy use for the space heating and space cooling was met according to the Norwegian passive house standards. The delivered energy to the building could be decreased by up to 55% in the second scenario.