Deep learning-based model predictive control with exponential weighting strategy and its application in energy management systems

The use of knowledge-based systems (KBS) in the development of energy management system (EMS) applications is addressed. Since the user interaction principles in today's EMSs are not considered good enough, a tentative specification for the next generation of EMS applications is proposed. The justification for the use of KBS technology in power system operation is discussed, and a prototype KBS development environment is presented. The goal of this project was to get security analysis tools into daily operation through a user-friendly and powerful man-machine interface. Dedicated hardware (LISP-machine) and sophisticated software tools (expert system shell) were used in the KBS development environment. >

Blockchain technology provides considerable promise to enhance security and transparency in energy management systems. As energy systems grow decentralized and involve various stakeholders, challenges including data integrity, transaction security, and transparency are becoming crucial. Traditional energy systems have risks such as cyberattacks, data manipulation, and inefficiencies owing to centralized control. Blockchain’s decentralized, immutable, and transparent ledger system can overcome these difficulties by enabling safe, verifiable, and tamper-proof energy transactions. Smart contracts further automate and ensure trust in energy trades. This article analyzes blockchain’s application in energy management systems, concentrating on decentralized energy trade, real-time grid balancing, and transparent billing. It also addresses the security implications of blockchain, its limitations, and future potential. While scalability and energy efficiency challenges persist, blockchain's role in constructing more resilient, secure, and transparent energy infrastructures shows promise for the future of energy management.

With the advent of smart grid concept, Internet of Things (IoT) and the deployment of smart meters, the cyber-attack threats on power networks have increased due to the use of communication systems that can be accessed by adversaries. Attackers will have the ability to manipulate the outcomes of smart meters which in turn influence the core application of Energy Management System (EMS): State Estimation (SE). Bad data analytic tools may fail to detect some attacks into measurements. Meanwhile, Machine Learning (ML) solutions have been proposed for detecting False Data Injection (FDI) attacks. However, there is a lack of ML time-series solutions presented in the state-of-the-art that is yet to be less complex. In signal processing, time-series solutions does not only consider the signal, but also the statistics of the signal over time. Therefore, in this paper, a machine learning for time-series solutions is presented as an application to model the measurements of the power grid that are used in SE. The presented model takes into account adaptive linear and non-linear filters: Finite Impulse Response (FIR), and Infinite Impulse Response (IIR). The presented models are implemented and performed on the IEEE-118 bus system. The results indicate the advantage of applying those filters over the state-of-the-art machine learning solutions.

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.

This paper describes a new method for enhancing the accuracy of network analysis applications in energy management systems. Topology processing, state estimation, power flow analysis, and contingency analysis play a key factor in the stable and reliable operation of power systems. In this respect, the aim of topology processing is to provide the electrical buses and the electrical islands with the actual state of the power system as input data. The results of topology processing is used to input of other applications. New method, which includes the topology error analysis based on inconsistency check, coherency check, bus mismatch check, and outaged device check is proposed to enhance the accuracy of network analysis. The proposed methodology is conducted by energy management systems and the Korean power systems have been utilized for the test systems.

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

Considering the global energy crisis and the need to reduce energy consumption while providing thermal comfort to occupants, building performance prediction using building simulation programs requires higher accuracy of output data. Therefore, it seems necessary to study the impact of occupant behavior, which is the main source of uncertainty in residential buildings. The traditional courtyard houses, which are recognized as a successful passive house model, respond to different climatic conditions. Therefore, this research focuses on this building type to analyze occupant window opening control scenarios and determine which control works better. For this purpose, several probabilistic controls and their effects on the adaptive thermal comfort of occupants in zones around a central courtyard were compared in the three cities of Yazd, Bandar Abbas, and Tabriz. Energy Plus was used as a simulation program for the application of Grasshopper's energy management system (EMS) along with the Ladybug and Honeybee environmental plugins. The results show that the window control algorithms can increase the adaptive thermal comfort of occupants by 25.7%, 32.2%, and 20.3% in each of the climates of Yazd, Bandar Abbas, and Tabriz cities, respectively. Indoor and outdoor temperature were the most significant variables for opening windows in the warm and cold seasons, respectively.

Application of Variable-Structure Control to Air-Conditioning Systems

The green industry is an industry that in its production process prioritizes the efforts to efficient and effective using of resources in a sustainable manner so as to be able to harmonize the industrial development with the environmental sustainability and can provide benefits to the community. One of the assessment criteria for the green industry is the energy aspect, namely industrial efforts in implementing energy efficiency, the usage of renewable energy, and conducting energy management activities. This research was conducted based on a case study of the application of Energy Management System at PT. X, which is one of the largest pulp and paper industry in Indonesia. The industry has an impact on reducing CO2 emissions through the implementation of Energy Management System. PT. X can save energy consumption by 47,700 GJ and reduce CO2 emissions by 11,805.75 tons CO2e, in 2017.

Saddle-point criteria and Wolfe-type duality for LU-$$(\Phi ,\rho )$$-approximated nonsmooth multiobjective optimization problems with applications in energy management systems

The state estimator is the main analysis tool in real-time operation of power systems. It obtains the operating condition of the network given a set of measurements and this is the first step in a series of automated applications in energy management systems. Specialized algorithms have been developed to perform Distribution System State Estimation (DSSE) since the Weighted Least Squares (WLS) estimator, commonly used in transmission systems, faces challenges due to specific characteristics of Distribution Systems (DSs). The WLS estimator requires the solution of a linear system which may be ill-conditioned due to a set of particularities of DSs such as the one, two and three-phase unbalanced branches, large number of nodes and connections between long and short lines, among others.

This paper presents a new decision tree (DT) based approach for fast voltage contingency screening and ranking for on-line applications in energy management systems. The hybrid decision tree model is developed to learn all the selected contingencies simultaneously, therefore fewer DTs are required. To reduce the size and improve the accuracy of the decision tree, the K-class problem is converted into the set of K two-class problems, and separate decision tree modules are trained for each of the two class problems. All the selected contingencies are presented to the filter module, which is trained to separate them in critical and non-critical contingency classes, which reduces the burden on ranking modular DT. The critical contingencies screened out by the filter module are presented to the ranking modular decision tree for their further ranking. To measure the severity of contingencies, bus voltage violation based scalar performance index is used. Full AC load flow is performed to generate the training and testing patterns for the proposed hybrid decision tree, under each contingency. The effectiveness of the proposed approach is tested on IEEE test systems. Once trained, a hybrid decision tree method gives fast and accurate screening and ranking of contingencies for unknown load patterns.

State estimation estimates the system condition in real-time and provides a base case for other energy management system (EMS) applications including real-time contingency analysis and security-constrained economic dispatch. Recent work in the literature shows malicious cyber-attack can inject false measurements that bypass traditional bad data detection in state estimation and cause actual overloads. Thus, it is very important to detect such cyber-attack. In this paper, multiple metrics are proposed to monitor abnormal load deviations and suspicious branch flow changes. A systematic two-stage approach is proposed to detect false data injection (FDI) cyber-attack. The first stage determines whether the system is under attack while the second stage identifies the target branch. Numerical simulations verify that FDI can cause severe system violations and demonstrate the effectiveness of the proposed two-stage FDI detection (FDID) method. It is concluded that the proposed FDID approach can efficiently detect FDI cyber-attack and identify the target branch, which will substantially improve operators' situation awareness in real-time.

The preparation of standards in the field of energy management of both international, regional, and national status by specialists of energy efficiency and energy saving problems, which are actively discussed around the world today, is an urgent problem. In this regard, the ISO 50001 Energy Management System standard allows organizations to prepare a policy package for more efficient use of energy to determine the objectives for the implementation of this policy use available information more effectively to make decisions about energy consumption; measure and evaluate the obtained results to evaluate the effectiveness of the policy continuously improve the energy management system. The main objective of ISO 50001 is to integrate energy efficiency into the existing management practices of organizations. The application of energy management systems as one of the main tools for the implementation of energy saving policy in enterprises based on the requirements of the ISO 50001 standard is very relevant, and the reasons for the need to apply energy management systems and relevant standards are asfollows Energy management system and standards as a component of national energy efficiency and renewable energy programs serves, energy management system accelerates technology improvements and innovations. ISO 50001 helps to develop a strategy to increase energy efficiency in buildings and structures, reduce costs and improve the environmental performance of operations.

It is important for energy managers to have a high level of knowledge and understanding of complex energy management systems. Energy practitioners need some basic informational and educational tools to help make decisions relative to energy management systems design, specification, procurement, and energy savings potential. In this chapter, we review advanced applications in Energy Management, Control, and Information Systems (EMCIS). The available features for these products are summarized and analyzed with regard to emerging trends in EMCIS and potential benefits.