Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.
Heating, ventilating, and air conditioning (HVAC) units account for a significant consumption share in buildings, namely office buildings. Therefore, this paper addresses the possibility of having an intelligent and more cost-effective solution for the management of HVAC units in office buildings. The method applied in this paper divides the addressed problem into three steps: (i) the continuous acquisition of data provided by an open-source building energy management systems, (ii) the proposed learning and predictive model able to predict if users will be working in a given location, and (iii) the proposed decision model to manage the HVAC units according to the prediction of users, current environmental context, and current energy prices. The results show that the proposed predictive model was able to achieve a 93.8% accuracy and that the proposed decision tree enabled the maintenance of users’ comfort. The results demonstrate that the proposed solution is able to run in real-time in a real office building, making it a possible solution for smart buildings.
There are many papers concerning the consumption of energy in different buildings. Most describe residential buildings, with only a few about office- or public service buildings. Few articles showcase the use of energy consumption in specific rooms of a building, directed in different geographical directions. On the other hand, many publications present methods, such as machine learning or AI, for building energy management and prediction of its consumption. These methods have limitations and represent a certain level of uncertainty. In order to compare energy consumption of different rooms, the measurements of particular building-room parameters were collected and analyzed. The obtained results showcase the effect of room location, regarding geographical directions, for the consumption of energy for heating. For south-exposed rooms, due to sun radiation, it is possible to switch heating off completely, and even overheating of 3 °C above the 22 °C temperature set point occurs. The impact of the sun radiation for rooms with a window directed east or west reached about 1 °C and lasts for a few hours before noon for the east, and until late afternoon for the west.
Buildings are envisioned to play an active role in future low-carbon energy systems. The complexity of building energy management systems increases as they interface more and more subsystems and domains. As an important step to achieve a higher technology readiness level, these energy management systems need to be systematically tested in real-life conditions. Currently, there are no standard testing and experiment strategies in buildings to handle the mentioned complexity. Additionally, the levels of details reported in the existing experimental studies are heterogeneous. This paper summarizes an application of a holistic testing method to a flexible fully-equipped prosumer with the goal of facilitating test preparation, execution, replication, and comparison. Several empirical suggestions are provided, and a hybrid quantification strategy with digital twins is presented.
This paper reports an experimental implementation of a flexible prosumer that adapts its behavior according to occupants’ objectives and system operator’s request. Model predictive control is incorporated into an existing building energy management system such that the energy management system can achieve user-defined objectives while quantifying energetic flexibility to support a stable and efficient system operation. Context-awareness is demonstrated through a series of experiments with energy efficiency, cost reduction, and carbon footprint reduction as occupants’ objectives. Besides, the flexibility of the prosumer is quantified in real-time and communicated to system operators. The results show occupants’ comfort and preference can be sufficiently guaranteed. Moreover, the flexibility quantification shows that the energy management system has considerable impacts on the levels of available flexibility.