• Energy Research

Indoor occupancy prediction can play a vital role in the energy-efficient operation of building engineering systems and maintaining satisfactory indoor climate conditions at the lowest possible energy use by operating these systems on the basis of occupancy data. Many methods have been proposed to predict occupancy in residential buildings according to different data types, e.g., digital cameras, motion sensors, and indoor climate sensors. Among these proposed methods, those with indoor climate data as input have received significant interest due to their less intrusive and cost-effective approach. This paper proposes a deep learning method called CNN-XGBoost to predict occupancy using indoor climate data and compares the performance of the proposed method with a range of supervised and unsupervised machine learning algorithms plus artificial neural network algorithms. The comparison is performed using mean absolute error, confusion matrix, and F1 score. Indoor climate data used in this work are CO2, relative humidity, and temperature measured by sensors for 13 days in December 2021. We used inexpensive sensors in different rooms of a residential building with a balanced mechanical ventilation system located in northwest Copenhagen, Denmark. The proposed algorithm consists of two parts: a convolutional neural network that learns the features of the input data and a scalable end-to-end tree-boosting classifier. The result indicates that CNN-XGBoost outperforms other algorithms in predicting occupancy levels in all rooms of the test building. In this experiment, we achieved the highest accuracy in occupancy detection using inexpensive indoor climate sensors in a mechanically ventilated residential building with minimum privacy invasion.

Mixing ventilation systems effectively improves thermal comfort in open-spaces due to adequate turbulent mixing of the cold stream with ambient air. This study introduces the concept of precision ventilation for achieving local thermal comfort in a mixing ventilation system. This precision ventilation system provides asymmetrical airflows from an active chilled beam (ACB) to each of the office occupants. These ACBs provide air velocities with different magnitudes and directions. To achieve different magnitudes and directions, JetCones are used to vary the airflow in different parts of the ACB. The performance of the precision ventilation system was analyzed using full-scale laboratory experiments and computational fluid dynamic (CFD) simulations. The full-scale laboratory experiments were conducted in a 4.2 m × 3 m × 2.8 m (L × W × H) thermal isolated room with an open-plan dual desk-chair setup. The jet-cones in the ACB unit were adjusted to throw the required amount of flow to the occupants. The occupants had different metabolic rates of 1.2, 1.4, and 1.6 in a warm office space. The room set point temperatures varied between 23 and 26 °C. The experimental and CFD results show that occupants facing symmetrical airflow distribution and with a constant 1.2 metabolic rate had a similar PMV index. The occupants with 1.2, 1.4, and 1.6 metabolic rate were exposed to asymmetrical airflows, i.e., 30%, 58%, and 70% of the total airflow. Occupants with higher metabolic rates were kept thermally neutral, in the −0.5 to +0.5 PMV range, by increasing the air velocity and room temperature to 0.4 m/s and 25 °C, respectively.

In forced air heating systems, there are basically two approaches to set the temperature of each zone within a multi-zone building: regulating the supply airflow rate and regulating the supply air temperature (or a combination of these two parameters). Conventional variable air volume (VAV) systems usually maintain the supply air temperature constant within all rooms and regulate only the supply airflow rate to the room. Recently, a novel multi-zone air heating and ventilation (MZHV) system has been developed, which is capable of regulating both the supply airflow rate and the supply air temperature to the rooms independently of each other. This paper investigates the flexibility of the novel MZHV system and compares it with the conventional VAV systems. The MZHV system as well as a multi-zone case study building are modeled in a high-fidelity simulation environment (IDA ICE). Instead of traditional rule-based control schemes, a model predictive control (MPC) scheme is developed to control real-time power consumption of the multi-zone building. For this purpose, a second-order state space model is identified for each zone and calibrated against the results from the high fidelity simulations. MPC is implemented in MATLAB environment by using the second-order state space models. Two co-simulations are performed to compare the flexibility of the novel MZHV system with a conventional VAV system. The results show that the MZHV system provides a significant flexibility in terms of the energy usage, without affecting the comfort levels. This flexibility can for example be exploited to reduce the overall energy usage or provide ancillary services for a smart electricity grid.

This paper is the second part of a two-part series which investigates the energy saving potentials in a novel mechanical ventilation system by replacing terminal dampers with decentralized fans. The present study (Part 2) proposes a method to control the main fan as well as the decentralized fans in such a way that the system is balanced for different demand requests. The control method comprises two levels, zone level and system level control, and is based on measuring static pressure in the duct system similar to the energy-efficient control proposed for the conventional ventilation system in Part 1. Two identical experimental mock-ups, one with the dampers and one with the decentralized fans were used in a laboratory environment to evaluate the performance of the novel ventilation system and to compare the two ventilation systems from power use point of view. Experimental results indicate the ability of the proposed method to control the ventilation system with decentralized fans. The measured power use of the two ventilation systems was almost the same for the range of tested total airflow rates, except when the total airflow rate was rather low. Calculations, however, reveal an energy saving potential of around 30% depending on the efficiency of the main fan.

Aggregators are emerging players in the future power markets which aggregate the flexibility of small consumers. This paper proposes a hierarchical model-based scheme to activate the energy flexibility of multi-zone buildings through a direct aggregation mechanism. The novelty lies in considering the power market mechanism in which consumers try to remain committed to their bids without violating their desired comfort levels. In the proposed approach, a high-level control layer determines an hourly energy budget for the whole building according to price signals and reports it to an aggregator. A lower-level dispatch layer then distributes the pre-planned hourly energy budget among different zones. At this level, the emphasis is on keeping the energy consumption as close to the pre-planned budget as possible while satisfying the comfort requirements. In addition, this layer computes the available real-time up and down regulating power and reports them to the aggregator. For comparison, we develop both a centralized and a decentralized model predictive control (MPC) scheme for the high-level control layer. Furthermore, a decentralized MPC with variable prediction horizon is designed for the lower-level dispatch layer. The proposed method is applied to a detailed multi zone building model developed in a high-fidelity simulation environment. The results show that the proposed scheme can keep its commitment to the aggregator to a large extent (by more than 93%) while maintaining the desired comfort levels. In addition, it is seen that the centralized model reduces energy costs and exhibits between 0.5% and 2.5% better commitment to the pre-planned budget in comparison with the decentralized one at the cost of sacrificing comfort to some extent. Moreover, some preliminary results regarding available up and down regulating power for residential buildings are reported for the first time.

In the context of increasing energy demands and the integration of renewable energy sources, this review focuses on recent advancements in energy storage control strategies from 2016 to the present, evaluating both experimental and simulation studies at component, system, building, and district scales. Out of 426 papers screened, 147 were assessed for eligibility, with 56 included in the final review. As a first outcome, this work proposes a novel classification and taxonomy update for advanced storage control systems, aiming to bridge the gap between theoretical research and practical implementation. Furthermore, the study emphasizes experimental case studies, moving beyond numerical analyses to provide practical insights. It investigates how the literature on energy storage is enhancing building flexibility and resilience, highlighting the application of advanced algorithms and artificial intelligence methods and their impact on energy and financial savings. By exploring the correlation between control algorithms and the resulting benefits, this review provides a comprehensive analysis of the current state and future perspectives of energy storage control in smart grids and buildings.

Exploitation of flexible consumption in the future smart grid requires new actors and infrastructure. In this paper, we propose a hierarchical setup in which a central controller, a so-called “aggregator,” is responsible for managing the flexibilities of industrial thermal loads via a contract-based direct control policy. The aggregator manipulates the consumption profile in an optimal and robust manner in order to provide upward and downward regulating power services. To this end, we consider a robust model predictive control design at the aggregator. The performance of the proposed controller is evaluated by simulating specific case studies involving a supermarket refrigeration system and a heating, ventilation, and air conditioning chiller in conjunction with an ice storage. In addition, we provide a comparison between heterogeneous and homogeneous aggregation of different thermal loads through simulation examples.

This article provides a state-of-the-art review on emerging applications of smart tools such as data analytics and smart technologies such as Internet-of-things (IoT) in case of design, management and control of energy storage systems. In particular, we have established a classification of the types and targets of various predictive analytics for estimation of load, energy prices, renewable energy inputs, state of the charge, fault diagnosis, etc. In addition, the applications of information technologies, in particular, use of cloud, IoT systems, building management systems (BMS) and building information modeling (BIM) and their contributions tomanagement of energy storage systems will be reviewed in details. The paper concludes by highlighting the emerging issues in smart energy storage systems and providing directions for future research. This article provides a state-of-the-art review on emerging applications of smart tools such as data analytics and smart technologies such as internet-of-things in case of design, management and control of energy storage systems. In particular, we have established a classification of the types and targets of various predictive analytics for estimation of load, energy prices, renewable energy inputs, state of the charge, fault diagnosis, etc. In addition, the applications of information technologies, and in particular, use of cloud, internet-of-things, building management systems and building information modeling and their contributions to management of energy storage systems will be reviewed in details. The paper concludes by highlighting the emerging issues in smart energy storage systems and providing directions for future research.