A Building Energy Management System Based on Facility Sensor Networks

Building energy management systems (BEMS) are integrated computerized systems that track and manage the energy use of many pieces of building-related machinery and equipment, including lighting, power systems, and HVAC systems. Modern buildings must have BEMSs in order to reduce energy usage while maintaining comfort. Not only for energy-saving purposes, BEMS is essential in enhancing the quality of the energy supply, which helps to gain a better understanding of how energy is used and the building's energy usage. When the dynamics of a building's energy usage are known, it is possible to determine which changes are most likely to reduce consumption. Numerous connected devices, operating modes, energy usage, and environmental factors can all be monitored and controlled in real-time using BEMS. Changing operating times and setting points to maximize comfort and efficiency is made simple by this. In this paper, we have primarily addressed the two significant issues of model optimization and electricity consumption forecasts. Future forecasting has been done using the LSTM based time series approach. We generated data on the amount of electricity consumed by a hospital facility and tested our suggested methodologies on actual data. The findings gained demonstrated that the strategies were successful with both types of data. On actual data, the trend in electricity consumption can be accurately predicted. Several model optimizers enhanced the suggested methods' performance as well. Our objective function gain accuracy result of 95%.

The present study provides potentially interesting for researchers in the novel integrated method of the fuzzy set qualitative comparative analysis (fsQCA) and digital design methodology to apply in the field of energy and sustainability of socio-technical systems (STSs) used in the building energy management (BEM). The research sample is the air-conditioning system (ACS). The social and technical aspects of ACS are satisfaction and energy consumption. Ten sustainability criteria are applied to assess and monitor the fields of energy consumption and satisfaction with the aim of achieving sustainable consumption in the use of ACSs for BEM. This action can be carried out by nesting a circuit in a microprocessor and microcontroller for use in a building energy management system (BEMS). According to, the circuit obtained from the assessment of the study can be practically implemented and assembled in a BEMS as online. This work can define a sustainability-based BEMS.

Given its adaptable and efficient energy consuming devices during peak hours, the residential building sector is urged to take part in demand response (DR) initiatives with the use of a building energy management system (BMS). The residents of buildings with BMS enjoy secure, pleasant, and fully managed lifestyles. Although the BMS helps the building consume less energy and encourages occupant engagement in energy-saving initiatives, unwelcome interruptions and harsh instructions from the system are inconvenient for the inhabitants, which further discourages their participation in DR initiatives. Building automation control is a crucial factor for improving buildings’ energy efficiency and management, as well as improving the electricity grid’s reliability indices. Smart houses that use the right sizing procedure and energy-management techniques can help lower the demand on the entire grid and potentially sell clean energy to the utility. Recently, smart houses have been presented as an alternative to traditional power-system issues including thermal plant emissions and the risk of blackouts brought on by malfunctioning bulk plants or transmission lines. This paper describes the necessary technology requirements and presents the methodology and the decentralized building automation novel algorithm for efficient demand side management in a building management system. Human comfort aspects including thermal comfort and visual comfort were taken into consideration when selecting heating and lighting controls. The suggested BMS relies primarily on a load-shifting technique, which moves controllable loads to low-cost periods to avoid high loading during peak hours. The model aims to minimize the individual household electricity consumption cost while considering customers’ comfort and lifestyle. All these are applied in an experimental university microgrid, and the results are presented in terms of energy saving in kWh, money in €, and working hours. The results demonstrated that the proposed approach might successfully lower energy use during the DR period and enhance occupant comfort.

In this article, a building electric energy prediction model using a Kriging method was developed for an efficient building energy management system (BEMS). In the prediction model, only easily obtainable weather factors such as temperature, humidity, wind speed, etc. were used as input parameters for actual application to the BEMS. In order to identify the effects of weather factors on building energy consumption, two data mining techniques were used: Analysis Of Variance (ANOVA) and Self-Organizing Map (SOM). The accuracy of the model using only easily obtain weather factors was compared with that of the model using the weather factors selected based on the results of data mining. According to the results, the building electric energy prediction model using only easily obtainable weather factors has sufficient predictive ability for BEMS. The developed building electric energy prediction model was applied to the optimization problem of charge/discharge scheduling for an electric energy storage system. The results showed that the building electric energy prediction model has sufficient accuracy for application to the BEMS.

A concept review of power line communication in building energy management systems for the small to medium sized non-domestic built environment

Demand response is proposed as a solution to handle the fluctuations in the power supply in a scenario with higher penetration of renewable energy sources. While demand response already offers a positive business case in certain domains it still lacks matureness in other areas, especially in the residential domain. This paper aims at providing a feasibility study of residential demand response by designing and validating a novel multi-modal Building Energy Management System (BEMS) that enables demand response provision and energy efficient control from residential buildings. The proposed consumer-centric BEMS monitors the building performance and its surroundings, interacts with the residents, optimally controls local Distributed Energy Resources (DERs) and provides demand response to an aggregator. The design decisions and the consequent architecture are detailed. A prototype of the envisioned BEMS has been developed and deployed in a testbed - a 12-storey residential building located in Denmark. The prototype performance, the data monitoring capabilities, interaction with the residents and controllability of local DER of the BEMS are demonstrated.

Building energy management is recognized as a useful technique to enhance building automation and facilitate demand response. This paper proposes a simple and useful building energy management system (BEMS) that simultaneously minimizes the user's electricity bill while assistants the utility to reduce the Peak-to-Average Ratio (PAR) of the regional load. The working mechanism of the proposed BEMS is based on a simple observation: the PAR reduction of an area can be achieved by reducing the PAR of individual buildings. The system consists of two stages: (1) in the first stage, the appliances are temporarily scheduled to obtain the minimum electricity cost of the unit; and (2) in the second stage, the appliances are re-scheduled with the objective to minimize the unit's PAR, while ensuring the building's electricity cost does not exceed the optimal cost determined in the first stage. Our numerical results show that by applying the proposed BEMS to individual units, the overall PAR of a group of buildings can be effectively reduced.

Energy systems are essential part of buildings and facilities, which are associated with high costs, and considered key success factor of businesses and services produced from the building or facility. Energy management will protect the underlying business by allowing accurate and automated management of energy systems and supply. Energy management systems (BEMS) are computer‐based automated systems that monitor and control all energy‐related systems from mechanical and electrical equipment in buildings. Building management systems (BMS) are commonly used to automate all services and functions within the building, which include energy management. BMS connects building components with a central computer to enable the control of different variables and parameters within the building. This chapter presents energy management system (EMS) and possible ways to achieve energy monitoring, savings, and smart homes. Case studies will be discussed to analyze energy savings and reduce electricity bills. To achieve full deployment and integration with smart grids, it is essential to implement BMS and BEMS.

The intricacies between building occupancy and its subsequent impact on core environmental variables such as temperature, humidity, lighting, and electrical usage are paramount to understanding and optimising energy consumption within building environments. Building Energy Management Systems (BEMS) play a pivotal role in harnessing real-time data to facilitate informed energy-saving decisions. In this study, we introduce a costeffective, scalable solution leveraging a network of low-cost sensors alongside ESP32 microcontroller technology to comprehensively monitor and analyse occupancy dynamics in real-time. This sensor network efficiently captures occupancy data, which is seamlessly integrated into a BEMS to provide a holistic understanding of how occupancy affects key environmental parameters and overall energy consumption. Through rigorous analysis, our system reveals significant correlations between occupancy levels and energy usage patterns, thereby enabling more efficient energy management strategies. The simplicity and affordability of our solution paves the way for widespread adoption, contributing substantially towards the broader goal of energy conservation in building ecosystems. These findings highlight the critical importance of continuous occupancy monitoring within BEMS frameworks to significantly curb energy wastage and foster sustainable building operations.

This paper presents the optimal operation of a building energy management system (BEMS), with combined heat and power (CHP) generation, thermal energy storage (TES), and battery energy storage (BES), subject to load demand uncertainty. The main objective is to reduce the total operating cost (TOC) and total CO2 emission (TCOE). First, we develop two models of load demand forecasting, one for weekday and the other for weekend, using artificial neural networks, long short-term memory, and convolutional neural networks. Then, we incorporate the predicted load demand and load demand uncertainty for planning the energy dispatch of the BEMS. TES aims to store the thermal energy waste from the power generation of CHP and discharge the thermal energy to the absorption chiller to supply the cooling load. BES and spinning reserve (SR) play an important role in handling the uncertainty of the load demand. The operation of BEMS, subject to the load demand uncertainty, is formulated as a linear program. We can efficiently solve the linear program and provide an optimal solution that satisfies the dispatch constraints. Thereafter, we determine the optimal size of BES, based on economics and environmental optimal operation. The proposed BEMS is compared to the previous BEMS, without BES and SR. Furthermore, we propose the multi-objective optimal operation, where the normalization for TOC and TCOE is introduced, and the multi-objective function is defined as a linear combination of normalized TOC and TCOE. The numerical results reveal the trade-off relationship between TOC and TCOE. In particular, when TCOE is minimum, TOC becomes maximum. On the other hand, when TOC is minimum, TCOE becomes maximum. The relationship provides a method to select the operating point, as well as analyze the power flow for the multi-objective optimal operation.

Effect of electrochromic windows on energy consumption of high-rise office buildings in different climate regions of Iran

Building energy management system(BEMS) in order to save the energy used in the operation stage of buildings has been actively introduced recently and it is recommended to apply BEMS to various certification system, but, there is a real situation such as low utilization level of comparison of energy savings and monitoring function oriented simple statistical processing. In this study, by using the principal component analysis(PCA) method that is able to analyze whether failure in repeater of measuring sensor for the chiller measurement data in the actual building where BEMS is operating, failures that exist in BEMS data were detected. PCA method is the most widely used method of adjusting the data and could detect a fault and improve the performance of diagnosis of low-dimensional description of data generated by the PCA method using multivariate statistics such as the Hotelling <TEX>$T^2$</TEX> statistics and Q-statistics to monitor the industrial system. It is found that because the end of operation time before replacing the sensor and Q-statistics beyond Q-Thresholds has been appeared to the same sample position, the failure of the sensor can be detected by PCA method using the stored BEMS data.

The available Building Energy Management Systems (BEMS), although they contribute to a significant reduction of energy consumption and improvement of the indoor environment, they can only be implemented in new buildings. Their installation in existing buildings is far from being cost effective due to the incompatibility of communication protocols between BEMS designed by various manufacturers and unavoidable modifications for data transmission. On the other hand, current research for energy efficient buildings has proved that although the design and the facilities including BEMS aim to satisfy the thermal and visual comfort plus the air quality demands while minimising the energy needs, they often do not reach their goals due to users interference. Latest trends in designing Intelligent Building Energy Management Systems (IBEMS) offer a Man Machine Interface that could store the users preferences and adapt the control strategy accordingly. The objectives of the present paper are to present the advantages of the use of a man machine interface based on a smart card terminal together with fuzzy control techniques in satisfying the users preferences plus to underline the capabilities that the LON network offers to the design. A fuzzy PID controller is developed to reach the first of the above objectives. The monitoring of the energy consumption along with satisfying the users preferences is achieved by the use of a suitable cost function for the whole system. All the above parameters as well as the cost function are kept between acceptable limits. The overall control system including the cost function is modeled and tested using MATLAB/SIMULINK. The implementation of the control system in an existing building requires interconnection of sensors and actuators installed across the building, is well served by the LonWorks technology due to its high standards and flexibility features.

The Building Energy Management System (BEMS), a well-known system that has been implemented in some energy corporations, has become attractive to many companies seeking to better monitor their energy consumption efficiency. This study investigated the external factors that influence acceptance of the BEMS from managerial perspectives. An extended model based on the Technology Acceptance Model (TAM) was created to evaluate the implementation of the BEMS in the manufacturing industries. A structural equation modeling (SEM) approach was used to analyze the model by adopting compatibility, features, technology complexity, and perceived risk as the external variables, and integrating the five dimensions of perceived ease of use, perceived usefulness, attitude, user satisfaction, and behavioral intention. The analysis results indicated that the external factors positively influenced users’ behavioral intention to use the BEMS through expected satisfaction, perceived ease of use, and perceived usefulness. Suggestions for BEMS developers are provided as well.

Building automation systems (BAS), or building control systems (BCS), typically consist of building energy management systems (BEMSs), physical security and access control, fire/life safety, and other systems (elevators, public announcements, and closed-circuit television). BEMSs control heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings; more specifically, they control HVAC?s primary components such as air handling units (AHUs), chillers, and heating elements. BEMSs are essential components of modern buildings, tasked with seemingly contradicting requirements?minimizing energy consumption while maintaining occupants? comfort [1]. In the United States, about 40% of total energy consumption and 70% of electricity consumption are spent on buildings every year. These numbers are comparable to global statistics that about 30% of total energy consumption and 60% of electricity consumption are spent on buildings. Buildings are an integral part of global cyberphysical systems (smart cities) and evolve and interact with their surroundings (Figure 1) [2]. As buildings undergo years of exploitation, their thermal characteristics deteriorate, indoor spaces (especially in commercial buildings) get rearranged, and usage patterns change. In time, their inner (and outer) microclimates adjust to changes in surrounding buildings, overshadowing patterns, and city climates, not to mention building retrofitting [3], [4]. Thus, even in cases of ?ideally? designed BEMS/HVAC systems, because of ever-changing and uncertain indoor and outdoor environments, their performance frequently falls short of expectations. Unfortunately, the complexity of BEMSs, large amounts of constantly changing data, and evolving interrelations among sensor feeds make identifying these suboptimal behaviors difficult [1], [5]. Therefore, traditional data-mining algorithms and data-analysis tools are often inadequate.