ENERGY EFFICIENCY EVALUATION IN MANUFACTURING THROUGH AN ONTOLOGY‐REPRESENTED KNOWLEDGE BASE

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.

To realize smart grid technologies such as load control, load leveling and demand response and solve problems caused by connecting many DERs to a distribution network, a distribution automation system (DAS) and energy management system (EMS) are respectively essential components. The communication of these systems has generally been handled by proprietary or vendor-specific protocols to date. Conversely, in intra- and inter-substation communication, the IEC 61850 has started to gain acceptance in supervisory control communication, due to its interoperability supporting multi-vendor environments. If the IEC 61850 can be applied to communications in DAS and EMS, this is expected to reduce the maintenance and equipment costs of each system. To do so, we must first evaluate the applicability of the IEC 61850 from the perspective of Logical Nodes (LN) and Data Objects (DO), which are data models defined in IEC 61850. This paper focuses on the DAS and building EMS (BEMS) and provides the evolution. We choose BEMSs because we expect many EMSs to be installed in homes or buildings in the near future and anticipate that building energy management functions will include home energy management functions. This evaluation result shows that most data items appearing on such communications can be represented as standard LNs and DOs but several data items cannot be easily represented as the same.

This paper reviews the work in the areas of machine learning applications for energy management in smart buildings, 5G technology’s role in smart energy management, and the use of machine learning algorithms in microgrid energy management systems. The first area focuses on the adaptability of building-integrated energy systems to unpredictable changes through AI-initiated learning processes and digital twins. The second area explores the impact of 5G technology on smart buildings, particularly in Singapore, emphasizing its role in facilitating high-class services and efficient functionalities. The third area delves into the application of various machine learning algorithms, such as supervised and unsupervised learning, in managing and monitoring microgrids. These broad areas collectively offer a comprehensive understanding of how machine learning can revolutionize energy management systems in smart buildings, making them more efficient, adaptable, and sustainable.

The energy management systems industry in the built environment is currently an important topic. Buildings use about 40% of the total global energy worldwide. Therefore, the energy management system’s sector is one of the most influential sectors to realize changes and transformation of energy use. New data science technologies used in building energy management systems might not only bring many technical challenges, but also they raise significant educational challenges for professionals who work in the field of energy management systems. Learning and educational issues are mainly due to the transformation of professional practices and networks, emerging technologies, and a big shift in how people work, communicate, and share their knowledge across the professional and academic sectors. In this study, we have investigated three different companies active in the building services sector to identify the main motivation and barriers to knowledge adoption, transfer, and exchange between different professionals in the energy management sector and explore the technologies that have been used in this field using the boundary-crossing framework. The results of our study show the importance of understanding professional learning networks in the building services sector. Additionally, the role of learning culture, incentive structure, and technologies behind the educational system of each organization are explained. Boundary-crossing helps to analyze the barriers and challenges in the educational setting and how new educational technologies can be embedded. Based on our results, future studies with a bigger sample and deeper analysis of technologies are needed to have a better understanding of current educational problems.

The implementation of monitoring tools and energy management systems (EnMSs) supports companies in their long-term energy efficiency strategies, and they are essential to analyse the effectiveness of energy performance improvement actions (EPIAs). The first fundamental step towards increasing energy efficiency is the development of energy audits (EAs). EAs provide comprehensive information about the energy usage in a specific facility, identifying and quantifying cost-effective EPIAs. The crucial role of these tools in clean energy transition is remarked by the European Energy Efficiency Directive (EED), which promotes the implementation of EAs and EnMS programmes. The purpose of this work is to better understand the link between EnMSs (specifically ISO 50001) and EAs in the EED Article 8 implementation in two industrial and two tertiary sectors in Italy. Moreover, the impact of company size, energy monitoring systems, and EnMSs on planned and/or implemented EPIAs is analysed. Our findings show that, albeit the complexity of the variables involved in energy efficiency gap, the “energy savings/company” and “EPIA/site” ratios are higher in enterprises with an EnMS and monitoring system. Thus, a correct energy audit must always be accompanied by a specific monitoring plan if it is to be effective and useful to the company decision maker.

Energy consumption in buildings is responsible for a significant portion of the total energy use and carbon emissions in large cities. One of the main approaches to reduce energy consumption and its environmental impact is to convert buildings into smart buildings using computer, software, sensor, and network technologies. Using smart building energy management systems provides intelligent procedures to control buildings’ equipment such as HVAC (heating, ventilating, and air-conditioning) systems, home and office appliances, and lighting systems to reduce energy consumption while maintaining the required quality of living in all of the building’s spaces. This chapter discusses and reviews utilizing cloud computing to provide energy-related services to enhance the operations of smart buildings’ energy management systems. Cloud computing can provide many advantages for smart buildings’ energy management systems such as providing the required software models that implement different control and monitoring algorithms and providing optimization methods for more efficient energy consumption in smart buildings. This chapter will also discuss the benefits and issues of utilizing cloud computing services for enhancing energy consumption in smart buildings.

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.

This study examines the implementation of an ISO 50001-compliant energy management system (EnMS) at the Saudi Standards, Metrology, and Quality Organization (SASO), a governmental organization in the Kingdom of Saudi Arabia, and the resulting environmental and business benefits. The methodologies used across three SASO sites (Riyadh, Jeddah, and Dammam), including energy audits, implementation procedures, and performance dashboards, are examined, supported by comprehensive data analysis and statistical regression, and the benefits in terms of energy reductions and cost savings are quantified. The obstacles to and drivers of implementation are also considered. The results show that, through strict adherence to ISO 50001 guidelines and the deployment of a range of energy efficiency measures, SASO has achieved a 39% reduction in electricity consumption since 2019, resulting in cost savings of approximately USD 3.6 million. The organization has also achieved a 38% reduction in CO2 emissions over the same period. This study demonstrates the pivotal role ISO 50001 plays in enhancing organizational sustainability and provides actionable insights into how it can be operationalized within the government sector to achieve significant environmental and economic benefits.

Energy management of HVAC systems in smart buildings by using fuzzy logic and M2M communication

The increasing power demand and the emergence of smart grids have given access to new opportunities for the energy management system of smart residential buildings that can reduce energy usage. Demand Response (DR) program is one of the most important strategies for implementing the residential building energy management system. The consumer load gets modified depending upon the electricity price provided by the utility so that the consumer electricity bill decreases. This strategy decreases the peak demand by scheduling the schedulable loads from the period when the electricity prices are high for the period when the prices are low with minimal effect on consumer comfort. It reduces the demand and increases the effective utilization of the energy of a residential building. This paper proposes an Effective Energy Management System (E-EMS) for residential buildings that models the problem by considering the uninterruptable and interruptible residential load appliances which can be controlled and their shifting windows constraints. It also manages the battery bank operation for maximum utilization of energy stored so that the wastage of power through dump load is minimum depending upon the Power Export Limit (PEL) defined by the utility. Further, the optimal sizing of renewable energy resources is also carried out for choosing the size of renewable energy resources and battery banks for effective utilization. The results of the proposed system are simulated by using MATLAB software to verify the effectiveness of the proposed strategy to control household appliances and their energy consumption by using BPSO.

Objective: This study aims to identify key characteristics of smart buildings that enhance sustainability and energy efficiency in educational institutions. By leveraging artificial intelligence, particularly machine learning, the study seeks to optimize energy consumption and improve learning environments. Theoretical Framework: Smart buildings integrate digital and communication technologies to enhance educational infrastructure. Despite their benefits, adoption in developing countries remains limited. This study builds on theories of sustainable energy management and computational optimization, emphasizing machine learning’s role in predictive energy modeling. Method: A machine learning model was developed to predict energy consumption in educational institutions. The k-nearest neighbor (k-NN) algorithm was applied using open-access data from the University of Durrës Building Energy Management System. Model validation was conducted through comparative analysis, assessing prediction accuracy and energy-saving potential. Results and Discussion: The model achieved an average relative error of 18.26%, confirming its predictive capability. Smart features enabled energy savings between 44.8% and 58.3%, depending on analyzed spaces. Additionally, an AI-powered interactive dashboard was proposed for real-time energy monitoring and optimization, aiding facility managers. Research Implications: This study underscores the role of smart buildings in promoting sustainability and reducing operational costs. AI-driven energy management systems can significantly enhance institutional sustainability practices. Originality/Value: This research demonstrates the effectiveness of machine learning in energy consumption prediction and proposes an AI-based solution for sustainable energy management in educational buildings. The findings provide valuable insights into integrating smart technologies for sustainability and efficiency.

Towards the integration of interconnected microgrids to deregulated electricity markets

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.

Sustainability on campus has become an increasingly important issue. In this paper, an integrated building energy and environment management system (BEEMS) on campus is proposed based on the campus network. The system includes two components: energy monitoring and environment monitoring. The system employs local network on campus and uses ZigBee technology to gather energy or environmental data from anywhere on campus. This system can supply rich analysis and report functions to monitor both the energy consumption and environmental parameters, which can ultimately improve the energy automation management level of the campus and help create a sustainable, healthy campus environment.

This study deals with the urban microgrid energy management that is dedicated to individual and collective self-consumption by providing flexibility to the distribution grid (DG). The proposed urban community microgrid is interconnected to a DG and it consists of an association of centralized storage units (called community energy storage system), community intermittent renewable generation, and intelligent energy management system (EMS). One of the main advantages of urban microgrid is that, in case of faults in the DG, it can cut existing interconnections and continue to supply the responsible community in the island mode. In this study, the developed urban microgrid EMS is based on the predictive control management through the day-ahead optimal power flow (DA-OPF) strategy. The main contributions of this work can be defined by two points. The first point is related to a development of the DA-OPF strategy for the urban microgrid based on the intelligent deep learning data forecasting and the mixed-integer nonlinear programming optimization methods. The second point concerns a development of an optimization function integrating the concept of ancillary services of DG flexibility. Experimental results and economical evaluation are presented in this article. By using the proposed strategies, it results in an important electricity price reduction for the considered urban microgrid, compared to a conventional distribution system and basic operation schemes.