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.

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.

A prominent feature of the future smart grid is the active participation of the consumer side in ancillary service provision. Grid operators procure ancillary services, including regulating power, voltage control, frequency control, and so on, to ensure safe, reliable, and high-quality electricity delivery. Consumers' involvement requires new entities and infrastructure. A so-called aggregator has been introduced as a new player to manage the services that are offered by the consumption units. This paper describes an industrial scale experimental setup for evaluating a particular type of aggregator. The aggregator aims to provide a distribution grid service from industrial thermal loads through a direct control policy. Our specific case studies are a supermarket refrigeration system and an HVac chiller in conjunction with an ice storage, which are virtually connected to the aggregator. Practical results obtained from testing on real industrial consumers demonstrate the theoretical studies of the aggregator design to a satisfactory level.

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.