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The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

While baseline energy modelling approaches for buildings are progressing to meet the demand for measurement and verification (M&V) of savings of an energy performance contract, more robust and insightful modelling approaches are needed. This study compares four baseline modelling approaches (e.g., change point, nonlinear autoregressive exogenous models (NARX), Gaussian process, and ensembles of trees) in terms of predictive accuracy and operational insights. The approaches were selected based on preliminary study and employed on pre- and post-intervention data gathered from twelve commercial buildings in Ottawa, Canada. Changes in the post-intervention energy use were estimated by comparing the predicted data with measured data for the individual buildings. The known primary intervention in these buildings was implementing commercially available smart building technologies that identify and address operational suboptimalities. A deeper analysis of one building’s automation system data indicates that energy savings were largely due to simple corrective actions such as eliminating the concurrent operation of heating and cooling coils of an air handling unit. The results show that saving estimates are largely affected by the choice of baseline energy modelling approach. NARX outperformed all other modelling approaches regarding accuracy and data fitting capability. On the contrary, the change point modelling approach offered insights into the operational performance of the buildings.

As current action remains insufficient to meet the goals of the Paris agreement let alone to stabilize the climate, there is increasing hope that solutions related to demand, services and social aspects of climate change mitigation can close the gap. However, given these topics are not investigated by a single epistemic community, the literature base underpinning the associated research continues to be undefined. Here, we aim to delineate a plausible body of literature capturing a comprehensive spectrum of demand, services and social aspects of climate change mitigation. As method we use a novel double-stacked expert—machine learning research architecture and expert evaluation to develop a typology and map key messages relevant for climate change mitigation within this body of literature. First, relying on the official key words provided to the Intergovernmental Panel on Climate Change by governments (across 17 queries), and on specific investigations of domain experts (27 queries), we identify 121 165 non-unique and 99 065 unique academic publications covering issues relevant for demand-side mitigation. Second, we identify a literature typology with four key clusters: policy, housing, mobility, and food/consumption. Third, we systematically extract key content-based insights finding that the housing literature emphasizes social and collective action, whereas the food/consumption literatures highlight behavioral change, but insights also demonstrate the dynamic relationship between behavioral change and social norms. All clusters point to the possibility of improved public health as a result of demand-side solutions. The centrality of the policy cluster suggests that political actions are what bring the different specific approaches together. Fourth, by mapping the underlying epistemic communities we find that researchers are already highly interconnected, glued together by common interests in sustainability and energy demand. We conclude by outlining avenues for interdisciplinary collaboration, synthetic analysis, community building, and by suggesting next steps for evaluating this body of literature.

Indoor environmental parameters have significant influence on commercial building energy consumption and indoor thermal comfort. Prediction of these parameters, especially that of indoor air temperature, along with continuous monitoring and control of real world parameters can aid in the management of energy consumption and thermal comfort levels in existing buildings. An accurate indoor temperature prediction model assists in achieving an effective energy management strategy such as resetting air temperature set-points in commercial buildings. This study examines the real indoor environmental data for multiple adjacent zones in a commercial building in the context of thermal comfort and identifies the possibility of resetting air temperature set-point without compromising the occupant comfort level. Also, the value of predicting the indoor temperature accurately in such a building is established through this case study. This study presents a nonlinear autoregressive network with exogenous inputs-based system identification method to predict indoor temperature. During model development efforts have been paid to optimize the performance of the model in terms of complexity, prediction results and ease of application to a real system. The performance of single-zone and multi-zone prediction models is evaluated using different combinations and sizes of training data-sets. This study confirms that evaluating the performance of the model in the context of major contributing aspects such as optimal input parameters and network size, optimum size of training data, etc. offers optimized model performance. Thus, when the developed model is used for long-term prediction, it provides better prediction performance for an extended time span compared to existing studies. Therefore, it is anticipated that implementation of this long-term prediction model will offer greater energy savings and improved indoor environmental conditions through optimizing the set-point temperatures.

The appropriate application of advanced control strategies in Heating, Ventilation, and Air-conditioning (HVAC) systems is key to improving the energy efficiency of buildings. Significant advances have been made in the past decades on model development to provide better control over the energy consumption of system components while simultaneously ensuring a satisfactory indoor environment in terms of thermal comfort and indoor air quality. Yet it is an ongoing challenge to select and implement the best-suited modeling technique for improving the control strategy of HVAC systems. For the development of modeling research it is important that the building research community is informed about the role, application, merits, shortcomings and outcomes of different modeling techniques used in HVAC systems. Even though several review articles have been published on modeling techniques, the weaknesses and strengths of these modeling techniques, along with performances of developed models associated with research studies, have rarely been identified. This study presents a critical review of current modeling techniques used in HVAC systems regarding their applicability and ease of acceptance in practice and summarizes the strengths, weaknesses, applications and performance of these modeling techniques. Additionally, the performance and outcome of some of the developed models used in real world HVAC systems have been discussed. From the extensive critical review it is evident that almost every model has a major/minor shortcoming generated from assumptions, unmeasured disturbances or uncertainties in some system properties. This review aims at highlighting the shortcomings of existing application-based research on HVAC systems, and accordingly, recommendations are presented to improve the performance of building HVAC systems.

The importance of predicting building indoor temperature is inevitable to execute an effective energy management strategy in an institutional building. An accurate prediction of building indoor temperature not only contributes to improved thermal comfort conditions but also has a role in building heating and cooling energy conservation. To predict the indoor temperature accurately, Artificial Neural Network (ANN) has been used in this study because of its performance superiority to deal with the time-series data as cited in past studies. Network architecture is the most important part of ANN for predicting accurately without overfitting the data. In this study, as a part of determining the optimal network architecture, important input parameters related to the output has been sorted out first. Next, prediction models have been developed for building indoor temperature using real data. Initially, spring season of Australia was selected for data collection. During model development three different training algorithms have been used and the performance of these training algorithms has been evaluated in this study based on prediction accuracy, generalization capability and iteration time to train the algorithm. From results Lovenberg-Marquardt has been found the best-suited training algorithm for short-term prediction of indoor space temperature. Afterwards, residual analysis has been used as a technique to verify the validation result. Finally, the result has been justified by applying a similar approach to another building case and using two different weather data-sets of two different seasons: summer and winter of Australia.

An efficient energy management system in commercial/institutional buildings can reduce energy consumption and operational cost and provide a comfortable and healthy indoor environment. However, without incorporating energy-efficient technologies and analysing the resulting performance the building energy management system may not provide effective control over energy consumption and the indoor environmental conditions. To verify system performance, it is necessary to study the building energy efficiency, examine building indoor environment and investigate existing operational strategies. This can eventually give the actual energy scenario of the building exploring problems existing within the system and opportunities for further upgrade paths that are both technologically and economically sustainable. To get the actual scenario of building energy management facilities, an institutional building of Murdoch University, Australia that incorporated state of the art technologies in the last two decades will be studied in this paper. Through this case study analysis salient information is revealed that will bring benefits to the energy management personnel as well as researchers in this area.

Highlights • The review of literature highlights performance gap in certified buildings. • The presented comparative analysis shows differences across certification systems. • This survey shows major discrepancies in data infrastructure requirements., While the research community widely recognizes the importance of post-occupancy measurement of buildings to verify performance, requirements of green building certification schemes are highly varied. To assess the effectiveness of building certification schemes during the operation and maintenance stage of certified buildings, this paper critically reviews recent case studies that report on post-certification performance. The review of relevant case studies from the literature reveals some important findings in relation to the performance gap of certified buildings. Subsequently, major operation and maintenance-related building certification schemes are surveyed to reveal the underlying reasons behind this performance gap. Post-certification actions that require post-occupancy data collection and analysis are identified through this survey and compared to highlight their strengths and shortcomings and pinpoint the major discrepancies in data infrastructure and archiving practices that hinder certified buildings in performing according to their design intent. Lastly, suggestions are extracted which may shed light on the re-certification pathways.

The ventilation component is one of the critical parts in an heating, ventilation and air-conditioning systems (HVAC) system and holds prime importance in ensuring operational efficacy and efficiency in terms of energy consumption and indoor environmental conditions. A true demand-based ventilation system with an economy air cycle not only offers reduced amounts of energy consumption but also can take part in a healthy indoor environment. This study provides true insight into the performance of a typical variable-air-volume air handling unit system under different operating conditions through a rigorous case study analysis in a real commercial building. To highlight pragmatic solutions to existing complexity within demand control ventilation strategies of the case building this study sets up real-time dynamic scenarios and investigates those scenarios in the context of indoor environment where both carbon dioxide (CO2) and volatile organic compound (VOC) concentrations are linked. To predict and monitor the indoor air CO2 concentration for the stated real time scenarios this study uses the mass balance equation for CO2 concentration. This study demonstrates that integrating the CO2 mass balance equation along with CO2 sensor data into the building management control system not only assists in reducing energy consumption but also takes part in a healthy indoor environment. This study suggests including the mass balance equation for CO2 concentration into the Australian ventilation standard AS 1668.2.

In the present research work the dynamic conditions of Multi S-shaped bladed rotor at different Reynolds number have been identified. The investigation on wind loading and aerodynamic effects on the four, five and six bladed S-shaped vertical axis vane type rotor has been conducted with the help of an open circuit subsonic wind tunnel. For different bladed rotor the flow velocities were varied from 5m/s to 9m/s covering the Reynolds numbers up to 1.35 x 105. It was experiential that by increasing the number of blades of rotor to the optimum limit considering all significant factors and at the same time by increasing its Reynolds number, the power output can be increased to its maximum level Finally, the nature of predicted dynamic characteristics has been examined by comparing with the existing research works. DOI: http://dx.doi.org/10.3329/jme.v44i1.19501

Indoor environmental parameters especially the air temperature have substantial effect on energy consumption in commercial buildings and indoor thermal comfort. This study presents a tuning approach of dynamic control strategy of temperature set-point with a view to improving occupants’ thermal comfort while simultaneously minimizing energy consumption. To determine optimum temperature set-points in response to ambient conditions, this study investigates the thermal comfort conditions of a commercial building based on real time series data. To quantify thermal environmental conditions for human occupancy, this study uses the graphical comfort zone method proposed by ASHRAE Standard 55-2017 through a rigorous analysis. Based on this analysis the study narrows down the comfort range in the context of seasonal variations and proposes tuning the Master Temperature Set-Points (MTSP) with 4.8°C variable linear band between upper and lower temperatures dependent on a simple algorithm. This re-setting strategy of temperature set-point ultimately offers extended lower and upper boundary limit for variable linear band. Extension of linear band for MTSP reduces the gap between temperature set-point and outdoor temperature which ultimately offers less heating and cooling energy consumption. Results show that implementation of this proposed approach would lead to monthly 2707.94 kWh energy savings either from heating or cooling or both during winter and summer season.