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• FDD tools are increasingly adopted in commercial buildings. • Using FDD tools to correct hunting faults can mitigate issues cost-effectively. • We developed and implemented automated control hunting correction in FDD tools. • The algorithm was tested in nine VAV boxes and successfully eliminated hunting faults. • We discussed challenges and future research areas for scaling the technology up. Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology. [ABSTRACT FROM AUTHOR]

The limited drive range (The maximum distance that an EV can travel.) of Electric Vehicles (EVs) is one of the major challenges that EV manufacturers are attempting to overcome. To this end, a simple, accurate, and efficient energy consumption model is needed to develop real-time eco-driving and eco-routing systems that can enhance the energy efficiency of EVs and thus extend their travel range. Although numerous publications have focused on the modeling of EV energy consumption levels, these studies are limited to measuring energy consumption of an EV’s control algorithm, macro-project evaluations, or simplified well-to-wheels analyses. Consequently, this paper addresses this need by developing a simple EV energy model that computes an EV’s instantaneous energy consumption using second-by-second vehicle speed, acceleration and roadway grade data as input variables. In doing so, the model estimates the instantaneous braking energy regeneration. The proposed model can be easily implemented in the following applications: in-vehicle, Smartphone eco-driving, eco-routing and transportation simulation software to quantify the network-wide energy consumption levels for a fleet of EVs. One of the main advantages of EVs is their ability to recover energy while braking using a regenerative braking system. State-of-the-art vehicle energy consumption models consider an average constant regenerative braking energy efficiency or regenerative braking factors that are mainly dependent on the vehicle’s average speed. In an attempt to enhance EV energy consumption models, the proposed model computes the regenerative braking efficiency using the instantaneous vehicle operational variables. The proposed model accurately estimates the energy consumption, producing an average error of 5.9% relative to empirical data. The results also demonstrate that EVs can recover a higher amount of energy in an urban driving environment when compared to high speed highway driving using the proposed model. Moreover, the study also compared different electric vehicles and quantified the impact of auxiliary systems, including the air conditioning and heating systems, on vehicle energy consumption levels using the proposed energy model. The study demonstrated that the use of the heating and air conditioning system could significantly reduce the EV efficiency and travel range. [ABSTRACT FROM AUTHOR]

The installation of smart meters has provided an opportunity to better analyze residential energy consumption and energy-related behaviors. Air-conditioning (A/C) use can be determined through non-intrusive load monitoring, which separates A/C cooling energy consumption from whole-house energy data. In this paper, a disaggregation technique is described and executed on 1-min smart meter data from 88 houses in Austin, TX, USA, from July 2012 through June 2013. Nineteen houses were sub-metered to validate the accuracy of the disaggregation technique. The R 2 value between the predicted and actual A/C energy use for the 19 houses was 0.90. The algorithm was then applied to all houses. On average, daily energy use from A/C increased by 25 ± 11 kWh between a mild temperature day of 15.5 °C (60 °F) and a hotter day of 31.5 °C (89 °F), with an 11 kWh increase just during peak hours (14:00–20:00). Average time operated, number of cycles, and A/C fraction of energy were found to increase linearly with outdoor temperature up to 25 °C (77 °F); a plateau was detected at higher temperatures. The accuracy of A/C disaggregation on 5-min data was found to be comparable to 1-min data. However, 15-min data did not yield accurate results due to insufficient granularity. [ABSTRACT FROM AUTHOR]

• Bringing the asset management of transformer under the purview of building manager from utility. • Derived mathematical model of demand response from transformer thermal model. • Method to estimate transformer temperature time constant from measurements and algorithm for improving the life of transformer by regulating HVAC and AHUs. • Smart grid architecture for demand responsive HVAC. • Real time simulation of impact of demand response using the actual building consumption data from an academic campus. Overloading and ageing effects contribute to 12-15% of distribution transformer failures in India. This research is motivated in extending life of transformer by using demand response as an operational strategy where the demand response events are issued by predicting overheating of transformer before it occurs. In this paper heating ventilation and air conditioning is considered as a flexible load for responding to demand response events. A demo site is created at 11 KV/440 V substation with 1.6 MVA transformer at Indian Institute of Technology Hyderabad (IITH) campus and the proposed architecture and algorithm is implemented. This substation supplies power to chiller plant and three academic buildings with heating ventilation and air conditioning (HVAC). Analysis for improving the transformer lifetime is carried out for 5 consecutive days during the period (25–29)th Feb 2020 using real time field data. It is observed that during this period 11 demand response events occurred with a total demand response of 919 kWH. From the obtained results it is observed that there is a saving of 15 hoursof transformer life within a period of 5 days (120 hours) of transformer operation. The key contribution in this paper is to bring together three concepts transformer lifetime estimation, HVAC flexibility and demand response for improving the transformer lifetime. [ABSTRACT FROM AUTHOR]

Highlights: [•] The optimal control gains were obtained from the linear matrix inequalities with mixed H 2 and H ∞ control algorithm. [•] The proposed method guaranteed convergence, stability, and provided a way for disturbance rejection and energy savings. [•] The coupling effects occurred among the compressor speed, opening degrees, evaporator temperatures and superheats. [•] The system identification and thermal dynamics equations could obtain the inner and outer loop transfer functions. [•] All simulations in this paper were in discrete time domain. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we outline a framework and the algorithms that will enable a home controller to accomplish occupancy-moderated multi-zone, multi-inhabitant space-conditioning under a demand-driven pricing scheme for electricity. We investigated the effects that influencing factors such as the number of zones and inhabitants, randomness of occupancy patterns and thermal mass of the residence can have on the efficacy of occupancy-moderated zonal space-conditioning (OZS) under different scenarios. Simulations driven by real data and highly realistic proxy data reveal that OZS can achieve improvements averaging 23% over the case without under certain settings. Our investigations also revealed that a house with heavier thermal mass stands to gain as much as 15% from OZS with pre-conditioning than a lighter one. We further offer a few pointers on reducing the cost of space-conditioning using OZS under a demand-driven pricing scheme to the average homeowner based on our findings. [Copyright &y& Elsevier]

Abstract: Modeling the performance characteristics of thermal systems has been a research interest for many decades with moisture transfer systems experiencing a resurgence over the last decade, especially in heating, ventilating, and air conditioning (HVAC) applications. In this study, a neural network (NN) model is developed to predict the heat and moisture transfer performances (i.e., the sensible and latent effectivenesses) of a novel HVAC energy exchanger called the Run-Around Membrane Energy Exchanger (RAMEE) which is able to transfer both heat and moisture between exhaust and supply air streams. The training data set for the NN model covers a wide range of design and operating parameters and is produced using an experimentally validated finite difference (FD) model. Two separate NNs (one for sensible and one for latent energy transfer) each with five inputs and one output, are selected to represent the RAMEE. The results from NN models are numerically and experimentally validated. The root mean squared error (RMSE) between the FD and NN models are 0.05°C and 2×10−5 kg v /kg a , indicating satisfactory agreement for energy exchange calculations. The paper reports the weights and biases to make the results of this study reproducible. These NN models are very fast and easy to use therefore, they might be used for design and for estimating the annual energy savings in different buildings which use the RAMEE in their HVAC system. Additionally, the NN models can be used with optimization algorithms to maximize energy savings and minimize life-cycle costs for a given system. [Copyright &y& Elsevier]

Abstract: A robust fault detection and diagnosis (FDD) strategy using a hybrid approach is presented for pressure-independent variable air volume (VAV) terminals in this paper. The residual-based cumulative sum (CUSUM) control charts are utilized to detect faults in VAV terminals. The residuals between the temperature error and its predication are generated using autoregressive time-series models. The standard CUSUM control charts are used to monitor the residuals which are statistically independent. If the CUSUM value exceeds the chart limits, it means the occurrence of fault or abnormity in the corresponding VAV terminal. The residual-based CUSUM control chart can improve the accuracy of fault detection through eliminating the effects of serial correlation on the performance of control charts. Also, the residual-based CUSUM control chart can enhance the robustness and reliability of fault detection through reducing the impacts of normal transient changes. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to isolate 15 fault sources. The FDD strategy was online tested and validated using in real time data collected from real VAV air-conditioning systems. [Copyright &y& Elsevier]

Abstract: Parallel flow condenser (PFC), which is widely used in automobile air conditioning (AAC) industries, was modeled and optimized in this paper. A sample of designed and manufactured condenser of this type was modeled and tested. In the proposed physical model the condenser is divided into three regions of superheat, saturated (two-phase) and subcooled. The modeling results validated by comparison with the experimental data. In optimization section, the condenser heat transfer rate was maximized while its pressure drop was minimized applying genetic algorithm multi-objective optimization technique. A set of Pareto optimal solutions as well as the final optimal design point were presented for our case study. The optimum design parameters resulted in heat transfer rate increase for 7.1% and decrease in pressure drop for 96% in comparison with the corresponding manufactured operating parameters. [Copyright &y& Elsevier]

Abstract: Indoor space cooling represents a large potential for solar energy use due to the relative coincidence between energy availability and cooling demand. Solar-assisted air conditioning (AC) applications emerged with the development of high efficiency solar collectors. Energy storage (hot or cold) must be implemented for solar-assisted AC applications when cooling demand is present during intervals without available solar energy and also for cooling capacity optimisation (“peak shaving”). The present paper analyses a solar-assisted ejector cooling system with cold storage. Simulations were carried out over one year considering climatic data for a hot location (Béchar, Algeria) and the performance of the system was assessed for a set of design conditions. Effects of cold storage upon comfort conditions and energy demand were evaluated. Maximum room temperature and overall interval of time during which the room temperature exceeded the set-point value were the parameters used to quantify system performance. It was found that cold storage improved comfort conditions compared to a system without storage. For some design conditions it was found that increasing the cold storage capacity did not result in improved comfort conditions. The control algorithm of the system was identified as the cause of this behaviour. [Copyright &y& Elsevier]

Abstract: A direct-expansion (DX) variable-air-volume (VAV) air-conditioning (A/C) system consists of a VAV air-distribution sub-system and a DX refrigeration plant. This paper reports in detail on a novel capacity controller developed for the DX VAV A/C system to regulate its compressor speed and hence its cooling capacity. The capacity controller consisted of both a numerical calculation algorithm (NCA), which was fundamentally based on the principle of energy balance using a number of real-time measured system’s operating parameters, and a dead-band for decoupling the control actions from both the capacity controller and a conventional PI feedback controller for regulating the opening of an electronic expansion valve (EEV) in the refrigeration plant. To study the feasibility of the capacity controller, an experimental rig for the DX VAV A/C system having two conditioned spaces was built and experimental tests were carried out. The test results showed that using the capacity controller, the cooling capacity of the system’s refrigeration plant can be accurately and continuously regulated and the supply air temperature well maintained at its desired value. The desirable independent zoning-control for space air temperatures can be successfully achieved by the DX VAV A/C system and the control performance for air temperatures in the conditioned space was highly satisfactory. [Copyright &y& Elsevier]

Abstract: The refrigerant mass flow distribution in a two phase fluid network of multi-connected air conditioning units (MAU) is generally uniform in the available literature based on the assumption that the fluid resistance of the pipes connecting the evaporators is ignored. However, such a fluid network of multiple evaporators and connecting pipes is actually a complex two phase direct return system, which leads to refrigerant flow distributary disequilibrium, for which few investigations have been conducted. In this paper, with pipe and local fluid resistance considered, fluid network theory was employed to develop a MAU fluid network model as a series of fluid circuits of components including single phase and two phase pipes, two phase electronic expansive valve (EEV), evaporator and pipes in parallel or series. A particular iterating control algorism was designed to distribute the refrigerant mass flow in proportion to the corresponding fluid resistance. For a given refrigerant fluid network with five indoor units, it is concluded by the model that the closer an indoor unit is to the centre of the fluid network, the less its mass flow deviates from its nominal value, and the units at the greatest distance from the centre of the fluid network are the ones with maximum disequilibrium. Furthermore, for a fluid network with multiple indoor units, an IDDR was proposed to evaluate the refrigerant distributary disequilibrium. [Copyright &y& Elsevier]

• A decentralized control method for multi-split air conditioning system is proposed. • Up to 8.6% energy savings are achieved with the proposed control strategy. • The EOTF-based PI controller has better robustness than model predictive controller. Multi-evaporator air conditioning system has found increasing applications in buildings. Energy-efficient control for this kind of system presents significant challenge because of strong coupling effects and various operation modes. Existing control methods are most likely to encounter uncontrollable problems and lack robustness under uncertain operation conditions. This paper proposes an energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning system. The proposed method is featured by a self-optimizing control strategy and a decentralized proportional-integral (PI) control algorithm based on the effective open-loop transfer function (EOTF). Different from most of previous studies that regulated compressor speed to control the evaporating pressure by using a supervisory optimizer, the proposed self-optimizing control strategy regulates compressor speed to maintain constant suction superheat and achieves energy-efficient operation in a simple and robust manner. Meanwhile, the EOTF-based decentralized PI algorithm overcomes the weakness of decentralized controllers in dealing with coupling effects, while retaining robustness against model errors and component faults. Control performance of the proposed method is tested using a validated system dynamic model. Through extensive controllability tests, it is found that the proposed control method significantly improves temperature control performance of the conventional strategy. Up to 8.6% energy savings are achieved with the proposed control strategy. Moreover, the proposed EOTF-based PI controller demonstrates enhanced robustness in comparison with an advanced model predictive controller. [ABSTRACT FROM AUTHOR]

• 1-D CNN is an innovative approach utilized for fault diagnosis in VRF system. • 1-D CNN models achieves good accuracy and is capable of fault diagnosis. • Enhanced the method to an ensemble model by using parallel 1-D CNNs. • The ensemble model's performance is improved and archived state-of-the-art accuracy. Variable refrigerant flow (VRF) systems are widely-adopted air conditioning systems. When system faults occur in VRF systems, the efficiency of VRF system will drop drastically. This paper presents a single 1-D CNN model and an ensemble model with parallel 1-D CNNs for diagnosing VRF system refrigerant charge faults under heating condition. From the cleaned experiment data of a commercial VRF system, 15 features are selected as the input for the proposed model with ReliefF algorithm. After training, the diagnosis accuracy of the single 1-D CNN model and ensemble 1-D CNN models is evaluated and compared with that of BPNN model and DT model. The result shows that both single 1-D CNN and ensemble 1-D CNN model can diagnose VRF system refrigerant charge fault effectively. The fault detection is also achieved in proposed models. The average diagnosis accuracy of 9-level refrigerant charge faults of the ensemble 1-D CNN model is up to 97.4%, surpassing that of BPNN model, SVM model, DT mode and DBN model. 1-D CNN based model is utilized for VRF system fault diagnosis for the first time, which lays a foundation for the expansion of the related researches. [ABSTRACT FROM AUTHOR]