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Research has revealed the existence of an energy-efficiency gap – the difference between optimal and actual energy end-use, suggesting that energy efficiency can be improved. Energy management (EnM) is a means for improving industrial energy efficiency. However, due to various barriers, the full potential of EnM is not realised. Several studies have addressed drivers and barriers to energy efficiency but few to EnM. This study aims to identify EnM practices, the most important perceived drivers and barriers for EnM, and relations among them in the energy-intensive Swedish pulp and paper industry (PPI), which has the longest experience internationally of practising EnM systems, and has worked according to the standards since 2004. Our results show that, altogether, the PPI works regularly and continuously with EnM, with a clear division of responsibilities. The highest maturity for EnM practices was for energy policy, followed by organization, investments, and performance measurement. The study also shows that communication between middle management and operations personnel has potential for improvement. The most important categories of drivers were economic, whereas for barriers they were organizational. Nevertheless, knowledge-related barriers and drivers were amongst the most important, suggesting that the absorptive capacity for energy issues could be improved. Image 1029 • Energy is managed continuously, with clear policies and responsibilities. • The main drivers are economical while main barriers are organizational. • Knowledge related barriers and drivers are amongst the next most important. • Employees' absorptive capacity for energy issues could be improved. • Link between energy policy and performance measurement could be improved. [ABSTRACT FROM AUTHOR]

The Swedish pulp and paper industry (PPI) must increase energy efficiency to remain competitive on the global market, which has experienced entries from countries with cheaper energy and raw material supplies. Interactions among variables for energy use, production, energy management, electricity price and firm characteristics (FC), in different types of mills, i.e., pulp, paper and integrated mills, in Sweden from 2006 to 2015 indicate that correlations among the studied variables were different in different types of mills. This difference between types of mills seemed to originate partly from varying accessibility to production residue that could be used for energy. For all types of mills, variation of electricity prices did not correlate significantly with energy efficiency during the study period. The studied FC were firm's age, number of employees, number of companies in company group, net sales and profit for the year. Energy efficiency was more affected by the variables characterizing energy and production compared to the variables representing FC. This study also suggested presence of possible discrepancies between FC that were perceived as barriers to energy management towards energy efficiency, according to previous studies, and what was shown by the data combining variables representing energy use, production and FC. [ABSTRACT FROM AUTHOR]

The pulp and paper industry faces several challenges linked to climate and environmental impact, resource efficiency, rising energy prices, increased competition for biomass resources, and declining demand for traditional printed paper products. However, these challenges also offer strategic opportunities for the industry to develop into a competitive, resource-efficient, and low-carbon industry in line with a biobased economy. Against this background, this paper aims to analyse current energy strategies in the pulp and paper industry in Sweden. Specifically, the paper analyses how companies combine continuous process efficiency to reduce energy costs with activities that could be developed into new energy-related products to increase revenue. Most of the analysed companies work to various degrees with both these strategies, employing methods that include improving energy efficiency, energy security, and energy conversion, as well as developing a wide range of biobased energy products. However, our study indicates that there is an untapped potential associated with energy product development, and we conclude that energy efficiency measures can free up resources, enabling the development of new energy products. Finally, several potential managerial outcomes and implications are outlined. • The paper analyses the energy strategies in the pulp and paper industry in Sweden. • Energy strategies focus both to improve energy efficiency and develop new energy-products. • The companies' energy strategies combined exploitation and exploration. • A number of managerial implications are presented. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is one of the five most energy-intensive industries world-wide. In Sweden, most pulp and paper mills were certified with a standardized energy management system already in 2005. As Swedish mills have more than a decade of experience with energy management systems and energy key performance indicators (KPIs), studying KPIs within Swedish pulp and paper mills will enable both a state-of-the-art positioning of best-practice in relation to energy KPIs in pulp and paper mills, but also spot potential barriers and drivers in the utilization of energy KPIs. This paper studies the current level of implementation and operationalization of energy-related KPIs in the Swedish pulp and paper industry. The results show a potential for improvement. • Pulp and paper mills' level of implementation of energy-related KPIs is studied. • 25% of the mills apply best practice regarding the establishment of energy KPIs. • Pulp and paper mills can potentially improve their monitoring of energy KPIs. • Lack of resources is a major barrier to implementing relevant energy KPIs. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

In recent years, the severe environmental degradation and high levels of fossil fuel consumption linked to conventional ship energy systems have drawn attention to the advancement of alternative ship energy systems. Consequently, ship energy systems based on the use of an electrical microgrid are coming to the fore as an increasingly popular alternative solution. However, managing the energy flows within a shipboard microgrid can be highly challenging due to the multiple energy sources (including renewable energy sources) and types of loads involved. Additionally, the integration of an energy storage system has been identified as an effective solution for improving the reliability of shipboard power systems, pointing out the important role of energy storage systems in maritime microgrids and their potential to enhance the energy management process. As such, effective energy management strategies are necessary to optimize performance and ensure that the vessel's energy demands are met while minimizing fuel consumption and reducing cost and environmental impacts. This paper presents a comprehensive review of such strategies and methods recently presented in the literature associated with energy management in shipboard microgrids integrating energy storage systems and examine the different techniques that can be utilized to achieve optimal system performance. Moreover, the paper also sheds light on the future challenges and perspectives imposed by the modern shipboard microgrids operation on the energy/power management of these systems. The aim of this paper is to provide a helpful guideline for researchers and engineers conducting research in the field of electrically powered ships. [Display omitted] • A comprehensive review of EMS for electrical and hybrid ship is presented. • A comprehensive review of ESS which can be used in such ships is presented. • Technologies of ESS and their suitability to ships are discussed and commented. • Future challenges and perspectives imposed by modern SMG operation are presented. [ABSTRACT FROM AUTHOR]

The customers of textile wet processing plants have committed to reduce their greenhouse gas emissions from outsourced manufacturing activities. The textile brands directly use specific energy consumption values to analyze and set steep, time-bound emission intensity reduction targets for the energy intensive textile wet processing plants. However, the direct use of specific energy consumption values to evaluate energy savings can be misleading. In this paper, the authors propose a methodological framework to systematically evaluate energy performance and monitor energy savings in the textile wet processing plants. Further, the approach was extended to analyze the energy performance monitoring practices and identify energy conservation measures. The proposed framework was developed based on thorough analysis of the manufacturing process of textile wet processing plants and the principles of ISO 50001. A textile wet processing plant was chosen as a case study for illustrating the effectiveness of the framework using multiple regression method. Bottom up approach for benchmarking was used and yet significantly simplified by the introduction of an innovative parameter "program mix factor" and thereby evaluate "equivalent specific energy consumption". The predicted values for specific energy consumption were found to be within a close error range of ±10 % of the actual data. The proposed methodological framework is therefore innovative, simple and yet comprehensive to conduct internal benchmarking in textile wet processing plants. The textile brands are suggested to use equivalent specific energy consumption instead of specific energy consumption for energy performance monitoring of their textile wet processing plant suppliers. The authors also conducted a survey in fifteen textile wet processing plants to assess the prevailing energy monitoring practices. The results of the survey suggests that only 20 % of the plants are practicing basic level energy performance monitoring, while the remaining 80 % are at minimal level. • Textile wet processing plants were analyzed to identify the parameters required to normalize specific energy consumption • A simplified methodological framework was developed for the textile wet processing plants and the textile brands • The framework serves as a useful tool to evaluate, monitor the realistic energy performance & identify conservation measures • The textile brands are suggested to use equivalent specific energy consumption to monitor energy performance • The study identified the low levels of energy monitoring practices in textile wet processing plants in South Asian countries [ABSTRACT FROM AUTHOR]

This paper addresses the design and implementation of a model predictive control framework for temperature control in buildings zones via direct control of their thermal energy inputs. Comfort-centric approach in ensured by selecting building thermal zones to be equal to the physical building rooms. The framework integrates different identification and estimation technologies, machine learning and model predictive control to assure systematic handling of non-modelled disturbances and offset-free control. It is envisioned as the lowest level in the hierarchical decomposition of building subsystems responsible for comfort and shaping the overall thermal energy consumption in building zones. The paper shows how it is deployed on a full scale occupied skyscraper building. To enable optimization of the whole building behaviour a special focus is put on developing the possibility for interaction and coordination with other building subsystems or energy distribution grids. This ensures the scalability of the approach, computational relaxation, technology independency, cost-effective implementation and enables upscaling towards the smart grid and smart city concepts where buildings play decisive roles. [Display omitted] • Direct control of thermal energy per zone. • Enabled interaction with other building subsystems. • Integral part for upscaling towards smart grid and smart city concepts. • Deployment and verification on a scale of the whole skyscraper building. • Modular service built on top of the existing building automation infrastructure. [ABSTRACT FROM AUTHOR]

Over the recent years, multimedia applications have begun to be used over wireless sensor networks (WSN), which hitherto has been primarily used to carry small amount of data. To meet the demand for higher throughput and real-time characteristics of multimedia contents, the routing protocol need to be simplified and optimized. In this paper, we present a routing algorithm that provides a low end-to-end packet delay and low packet loss, referred to as smart, greedy forwarding algorithm based on throughput and energy-awareness (SGFTEM). This algorithm routes multimedia packets across a WSN by choosing high- throughput paths rather than always choosing the shorter path to the sink. When it encounters network holes, a network void-bypass is applied, which enhances the reliability of the network. Energy management is also implemented to extend sensor node lifetime by way of reducing the radio transmission coverage to a suitable distance reachable by the sensor nodes The simulation results shows that, SGFTEM reduces the end-to-end delay by 40% and packet loss ratio by 35% and maintain energy consumption against other algorithms. With energy management, the residual energy of sensor nodes are made to be equally distributed across the entire network which results in load balancing. The performance of SGFTEM are also compared via simulation against AGEM, TPGF, GPSR, and AODV showing superior performance under similar scenarios i.e. low packet end-to-end delay, low packet loss and load balancing of routing paths. [ABSTRACT FROM AUTHOR]

The landscape of Demand-Side Energy Management (DSM) research is rapidly evolving, shaped by technological innovations and policy developments. This paper presents an exhaustive bibliometric analysis and methodological framework to explore the research trends within the DSM domain. By synthesizing data from Scopus and OpenAlex, we compile a comprehensive dataset of DSM publications that serve as the basis for our analysis. Through rigorous data acquisition and cleaning, we ensure the reliability and relevance of our dataset. We employ state-of-the-art Large Language Models (LLMs) and topic modeling techniques, including GPT and BERTopic, to perform semantic analysis and uncover thematic structures within the literature. Statistical analysis of the literature dataset reveals a steady increase in DSM publications, with significant contributions from prestigious journals and institutions worldwide. We observe that articles are the predominant publication type, while reviews often cite more references and receive higher citation counts. The distribution of publications over time indicates a growing interest in DSM, particularly since 2014. Geographical mapping of institutions highlights key regions contributing to DSM research, with notable outputs from Europe, North America, and East Asia. Coupled with citation network analysis, our approach reveals the influential works and emerging trends that define the scientific progression of DSM research. Our unsupervised topic modeling, powered by BERTopic, clusters the publications into distinct themes, while our advanced visualization techniques using UMAP and t-SNE provide insights into the semantic space of DSM literature. The resulting thematic classification is presented in a hierarchical structure, offering a comprehensive understanding of the field's focus areas. Our citation network analysis, utilizing force-directed graph computation and edge-bundling algorithms, maps the interconnectivity and impact of research contributions, providing a dynamic view of the field's evolution. This study not only charts the landscape of DSM research but also offers a methodological blueprint for future bibliometric analyses. The insights gained from this multi-faceted exploration serve as a valuable resource for researchers, policymakers, and industry practitioners looking to navigate the complexities of DSM and contribute to its scientific advancement. • Reviews energy demand-side management literature using bibliometric methods. • Uses citation network to reveal research clusters and interdisciplinary connections. • Uses GPT embedding and BERTopic model to cluster literature into ten themes. • Develops software tool for bibliometric analysis. [ABSTRACT FROM AUTHOR]

• TIP-EMS enhances EV energy efficiency by integrated prediction and energy management. • Significant driving economy boost; 1.6%-4.2% better than state-of-the-art PEMS. • TIP-EMS shows robust performance with varying prediction weight adjustments. To enhance the energy efficiency of electrified vehicles (EVs), developing effective energy management strategies (EMS) for hybrid storage systems is essential. Predictive EMS (PEMS) that foresee future vehicle speeds have demonstrated substantial potential in boosting EMS performance. However, traditional PEMS models, employing a sequential approach of speed prediction followed by energy allocation, are hampered by cumulative errors. These errors from the initial speed predss this issue, this paper introduces a novel solution: the trainable integrated preiction negatively impact the efficiency of subsequent energy management. To addrediction and energy management strategy (TIP-EMS). Contrasting with conventional sequential PEMS, TIP-EMS features a dual-branch, integrated neural network, which is fully trainable. This network processes driving status inputs via attention layers, with one branch dedicated to energy management objectives using a reinforcement learning (RL) algorithm, and the other to vehicle speed prediction. Both branches are trained simultaneously, but post-training, only the RL branch is activated for energy management. Implemented with a soft actor-critic RL algorithm, TIP-EMS is applied to a fuel cell EV for optimized energy management. The validation involved training TIP-EMS using 27 driving profiles, which developed its prediction and energy management capabilities, followed by tests in untrained scenarios. The results show that TIP-EMS surpasses conventional sequential PEMS by up to 4.2% in scenarios where prediction accuracies are comparable, highlighting the efficacy of the trainable integrated mechanism. In addition, TIP-EMS exhibits superior energy conservation compared to non-predictive RL strategies. Lastly, TIP-EMS exhibits robustness to adjustments in the weight given to the prediction objective, further confirming its practical applicability. [ABSTRACT FROM AUTHOR]

Improving energy efficiency has important significance for hybrid electric UAVs. This paper aims at improving energy efficiency by maximizing utilization solar energy, reducing demand power and optimizing power allocation. The flight trajectory optimization and energy management are considered synthetically, and the coupled model is established. Adaptive sequential convex programming method (ASCP) is further proposed to solve this coupled model. The adaptive discretization method, fuzzy-based trust region update mechanism and two-stages solving strategy are proposed to improve its optimality and convergence. The optimized flight trajectory and global optimal battery S O C trajectory can be applied to trajectory tracking control and online energy management. Numerical simulation results show that ASCP can improve energy efficiency of hybrid electric UAVs. Compared with uniform discretized sequential convex programming (UDSCP) and Gauss pseudo-spectral method (GPM), it can improve solar energy by 9.3% and 24.1%. Energy management experiment results indicate that ASCP has excellent energy saving effect. Compared with nonlinear model predictive control (NMPC), fuzzy logic state machine (FLSM) and passive energy management strategy (PEMS), it can save hydrogen by 13.5%, 18.3%, and 22.2%, respectively. This work is conducive to promoting the application of renewable energy in UAVs. • Energy efficiency is improved by flight trajectory and energy management coupled optimization. • The flight trajectory optimization and energy management coupled model is established. • Adaptive sequential convex programming method is proposed to solve the coupled model. • Fuzzy logic control is proposed to adaptively adjust the sizes of trust region. [ABSTRACT FROM AUTHOR]

From an environmental point of view, reduced use of energy remains a cornerstone in global greenhouse gas mitigation. However, without full internalization of external costs, greenhouse gas mitigation as such may not be highly prioritized among business leaders. Rather, it is the magnitude of production costs and ultimately the size of market revenue that articulates success or failure for business leaders. Nevertheless, reduced energy use or improved energy efficiency can have a vast impact on profitability even for companies with low energy costs, as the reduced energy costs directly lead to increased profits. In this paper, a review of ten years of empirical research in the field of industrial energy management in Swedish industry is presented. Based on the review, the paper proposes success factors for efficient energy management, factors which could help guide individual energy managers as well as policy makers in order to close the energy efficiency and management gaps. The paper also presents an overview of important industrial energy management tools, which would facilitate in-house energy management in industry. [ABSTRACT FROM AUTHOR]

The energy benchmarking has been recognised as an effective analytical methodology and a significant management tool that helps to improve the efficiency and performance of the energy utilisation. Studies on energy benchmarking has been worldwide carried out in many areas for different objectives. However, there are few systematic theories to support the development of a reasonable energy benchmarking and to provide efficient application measures in machining systems. To overcome these challenges, based on previous studies we propose two significant rules of energy benchmarking in machining systems contributing to the development of the energy benchmarking and application. This paper describes the concept, content and scope of the energy benchmarking rules laying a solid theoretical foundation for the energy benchmarking research in the machining field and giving the research directions. Meanwhile, through application analysis of the rules, the energy benchmarking rules have wide application prospects in machining systems and play an important role in the energy-efficient production. [ABSTRACT FROM AUTHOR]

Network end-user devices such as laptops and desktop PCs are often left powered ON 24/7 while they remain idle most of the time. The main reason behind this is maintaining network connectivity for remote access, VoIP, instant messaging and other Internet-based applications. The Network Connectivity Proxy (NCP) emerged as a quite promising strategy for significantly reducing network energy waste by allowing devices to sleep without losing their presence over Internet. It impersonates presence of devices during their sleeping periods at all layers of TCP/IP stack. The NCP can successfully proxy basic networking protocols (e.g., ARP, PING, DHCP, etc.) but faces challenges in proxying TCP sessions and proprietary closed-source applications with encrypted packets. To overcome the limitations of NCP concept, this paper proposes a new strategy for reducing network energy waste through intelligent coordination among a user's devices (e.g., smartphone, laptop, desktop PC, etc.). In the proposed system, a user's daily used devices autonomously and seamlessly communicate with a centralized control server that decides which user device will maintain presence of applications at a given instant. To allow devices to sleep and wake them up whenever necessary, the proposed system also uses a light-weight Home Gateway Proxy (HGP) with very basic set of practically realizable features. The communication framework for HGP is designed to achieve auto-discovery, seamless networking and communication features. This paper also practically evaluates the proposed system and estimates energy saving for fixed as well as mobile network devices. [ABSTRACT FROM AUTHOR]

This paper reports the use of low cost off-the-shelf components to develop a wireless energy monitoring system for the purpose of non-intrusive appliance load monitoring. The system comprises hardware design where a standard open source WiFi-connected embedded system board is used as the core component, an analog front end for interfacing between the voltage and current sensors and the embedded board and also a server to store data for further processing. The software side consists of algorithms for performing data partitioning and Naive Bayes classifier. Non-intrusive appliance load monitoring (NIALM) is a methodology used to disaggregate total power consumption into individual electrical appliance power usage. In this paper, the average power consumptions of electrical appliances are obtained from the system setup through the identification of switching ON or OFF events of appliances. The Naive Bayes classifier is deployed for appliances’ classification. This work demonstrates the feasibility of utilizing low cost and standard components to develop a wireless energy monitoring system. It is also observed that the developed classifier algorithm is able to classify individual electrical appliances with satisfactory accuracy level using few training examples. The novelty of this paper is that it developed a data set consisting of 4 types of appliances from the application of embedded networking device. This paper also shows that satisfactory classification accuracies can be achieved using few training examples with generic classifiers (Naive Bayes) and one feature (the average power). It provides an example of how NIALM can offer feedback to appliances’ energy consumption and therefore promote energy efficiency. [ABSTRACT FROM AUTHOR]

This paper focuses on energy management in an ultra-energy efficient vehicle powered by a hydrogen fuel cell with rated power of 1 kW. The vehicle is especially developed for the student competition Shell Eco-marathon in the Urban Concept category. In order to minimize the driving energy consumption a simulation model of the vehicle and the electric propulsion is developed. The model is based on vehicle dynamics and real motor efficiency as constant DC/DC, motor controllers and transmission efficiency were considered. Based on that model five propulsion schemes and driving strategies were evaluated. The fuel cell output parameters were experimentally determined. Then, the driving energy demand and hydrogen consumption was estimated for each of the propulsion schemes. Finally, an experimental study on fuel cell output power and hydrogen consumption was conducted for two propulsion schemes in case of hybrid and non-hybrid power source. In the hybrid propulsion scheme, supercapacitors were used as energy storage as they were charged from the fuel cell with constant current of 10 A. • Driving strategy to minimize energy consumption was determined at given restrictions. • Driving energy is related to acceleration phase when average speed is determined. • Fuel cell degradation requires modified energy management strategy. • Hydrogen consumption was evaluated for hybrid and non-hybrid propulsion. [ABSTRACT FROM AUTHOR]

The advent of complicated embedded systems with regard to relentless technology scaling and integration of more components into a single chip, have caused these systems to be less reliable. Moreover, these advancements have accompanied with a drastic increase in energy consumption. However, using energy management techniques such as Dynamic Voltage and Frequency Scaling (DVFS) has made reliability issues to be exacerbated. Hence, energy efficiency and fault-tolerance are two conflicting key objectives in the design of efficient real-time embedded systems. In this paper by considering periodic real-time tasks in standby-sparing system, we propose a novel online scheduling technique, which tackles this problem. The aim of this paper is twofold: (1) to show that scheduling primary and backup tasks in different processors in a moderate speed for static-priority real-time tasks will lead to better energy savings; (2) to explore the effectiveness of this technique for mixed-criticality tasks. Our simulation results reveal a significant energy saving (up to 25%) in comparison with the state-of-the-art scheme based on standby-sparing system, while preserving system's original reliability. [ABSTRACT FROM AUTHOR]

• Allowing for energy-efficient controls adaptive to diverse production disturbances. • Modeling the system dynamics of serial production lines using max-plus algebra. • Designing an adaptive energy-efficient control policy based on opportunity windows. • The diversity of production disturbances significantly affects energy waste. • Achieving more energy savings with less throughput loss than other control methods. The energy-efficient control has emerged as one of the most promising measures to reduce the unproductive energy consumption of machines during production disruptions. Diverse production disturbances (PDs) are the fundamental cause of production interruptions that result in unproductive energy consumption. In this scenario, the diversity and randomness of PDs necessitate elaborative consideration in energy-efficient controls. This consideration is crucial to the effectiveness of energy-efficient controls, yet it has been generally overlooked. To this end, this paper presents an adaptive energy-efficient control (AEC) methodology to generate adaptive control decisions when machines encounter diverse PDs in serial production lines. As a first step towards AEC, a PD identification method driven by Markov Logic Network is introduced to identify ongoing PDs. To then estimate the system performances in terms of energy savings and throughputs, a system dynamics model for serial production lines is established based on max-plus algebra. Finally, an AEC policy is formulated to optimize unproductive energy consumption on a nearly global scale while maintaining throughputs. A case study was performed on a die-casting production line in order to validate the effectiveness of the methodology. The results indicated a 96.1% F 1 score for PD identifications. Based on this, the AEC policy resulted in a 61.12% reduction in unproductive energy consumption, with a corresponding decrease in throughputs of only 0.65%. It was confirmed that the AEC methodology achieved greater energy savings while minimizing throughput losses compared to conventional energy-efficient control methods. [ABSTRACT FROM AUTHOR]

Diffusion of Renewable Energy Technologies (RETs) governs by the status of technology in terms of efficiency and techno-economical feasibility. The states plan for the deployment of resources for the development with special reference to sustainable environment. The demand and supply energy model help to provide more focus on the long term goals. The theory of diffusion modeling allows analysis of diffusion processes and study of growth rates of different technologies and underlying diffusion factors of the particular states, particularly for Indian 8 National Missions implication factors concerning to National Action Plan for Climate Change (NAPCC). The Energy Management Systems (EMS) and Distribution Management Systems (DMS) provide control on hybrid sources of energy generation and distribution over on-grid and off-grid applications. Energy Distributive system model for adequate accurate predictive analysis plays important role for sustainable country energy resources, in consideration of all influential factors in energy generation and distribution. For prediction aspects the important parameters are geographical location, seasonal influence, effect of climate change and state or area concession. With the abundant data and relative economic indicator, energy prediction is performed with close loop predictive system based of timing algorithm. The energy economics in free trade market like India, where the peak load varies abruptly due to season and community demand, its hourly prediction model is more useful. The energy model provides the estimation and prediction of hybrid power generation in consideration of the parameters on resource potential, technology, efficiency and consumption pattern. The monitor, statistical and prediction model with inbuilt mechanism for renewable energy certificate (REC) and perform, achieve and trade (PAT) incentives are explored in this paper. The paper presents energy models to enhance knowledge and skills in the efficient transfer and management of technology for optimally allocating different types of renewable resources, technology and economic feasibility. [ABSTRACT FROM AUTHOR]

Context: Definition of a comprehensive facility data model is a prerequisite for providing more advanced energy management systems capable of tackling the underlying heterogeneity of complex infrastructures, thus providing more flexible data interpretation and event management, advanced communication and control system capabilities. Objective: This paper proposes one of the possible implementations of a facility data model utilizing the concept of ontology as part of the contemporary Semantic Web paradigm. Method: The proposed facility ontology model was defined and developed to model all the static knowledge (such as technical vendor data, proprietary data types, and communication protocols) related to the significant energy consumers of the target infrastructure. Furthermore, this paper describes the overall methodology and how the common semantics offered by the ontology were utilized to improve the interoperability and energy management of complex infrastructures. Initially, a core facility ontology, which represents the generic facility model providing the general concepts behind the modelling, was defined. Results: In order to develop a full-blown model of the specific facility infrastructure, Malpensa and Fiumicino airports in Italy were taken as a test-bed platform in order to develop the airport ontology owing to the variety of the technical systems installed at the site. For the development of the airport ontology, the core facility ontology was first extended and then populated to reflect the actual state of the target airport facility. Conclusion: The developed ontology was tested in the environment of the two pilots, and the proposed solution proved to be a valuable link between separate ICT systems involving equipment from various vendors, both on syntax and semantic level, thus offering the facility managers the ability to retrieve high-level information regarding the performance of significant energy consumers. [ABSTRACT FROM AUTHOR]

Energy management systems are considered as appropriate instruments to identify and realize energy efficiency potentials in industrial plants. Studies point out a largely untapped potential for energy efficiency in industry through the adoption of systematic energy management. We suggest introducing qualitative and quantitative modelling tools into energy management to facilitate a focused process to set priorities and identify effective measures. This paper describes the case of voestalpine Austria Draht GmbH, an Austrian wire rolling mill. voestalpine Austria Draht GmbH at present is implementing a knowledge based energy management system. Within the work described in this paper, first an overview of energy flows using Sankey diagrams was elaborated. Then a qualitative systems analysis was developed for the plant and a gap analysis done for priority areas. Subsequently physical input–output models for several energy intensive units were prepared. The paper describes the models for the cooling water system and product cooling. This study revealed a significant potential to save electricity by optimizing the control of cooling water pumps along the milling train and to recover heat from the final products. 2 GWh of electricity can be saved annually. The biggest single energy loss in the mill is the heat from air cooling the products on hook conveyors. These heat flows contain about 30–50 % of the energy input for heating up the billets. Following a theoretical analysis of the potential, a demonstration plant was developed and pilot tests were run to characterize the realistic potential of heat recovery on the hook conveyor. Based on the test results, feasibility studies for large scale plants were carried out. As a result, 0.8 GWh of heat can be recovered annually. The method was found useful because it helped to structure the existing information with a reasonable effort mostly by the operators themselves in their work and to build a conceptual model of energy flows and energy efficiency of the total plant. [ABSTRACT FROM AUTHOR]

One of the main pillars of the modern industry is the uninterrupted supply of energy with a reasonable price. The energy production is based mostly on non-renewable energy resources that are getting more expensive progressively. This increase in energy cost consequently leads the companies to a more expensive production of goods. Therefore it has been established that energy efficiency is not only very important for the environment but also for the sustainable production in the manufacturing companies. Effective energy management plays an important role in the necessary increase of energy efficiency in industry. Next to the expansion of regenerative energies, it establishes a further pillar at closing the energy supply gap resulting from the energy transition. This paper presents the current energy situation in Germany and its impact on the German industry. Furthermore this paper presents a methodology which can be used by organizations to systematically implement energy management system. Eventually the paper provides a model that illustrates the realization of a PDCA cycle which is necessary for energy management in the organization. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a hybrid power system suitable for powering electric cars, trains and aircraft especially under high fluctuated load demand. The hybrid system includes fuel cells (FC), batteries and supercapacitors (SCs). The energy management strategy (EMS) is a key factor to reduce the total hydrogen consumption and slow down the FC performance degradation. A new EMS based on a recent optimization technique named Salp Swarm Algorithm (SSA) is proposed taking into consideration that the load demand is fully satisfied within the constraints of each energy source. The main objective of the proposed strategy is to minimize the total hydrogen consumption of the system. To minimize the energy obtained from the FC, the energy supplied by the batteries and supercapacitors is maximized. The SSA is an efficient and simple optimizer that needs few numbers of control parameters to be adjusted compared to other optimization algorithms. In order to show the validity of the proposed approach, a comparative study with other conventional approaches such as classical proportional-integral control strategy, frequency decoupling, and state machine (FDSM) control approach, equivalent consumption minimization strategy (ECMS), external energy maximization strategy (EEMS), and genetic algorithm (GA) is presented. In this study, the capstones of the comparison are the total H 2 consumption of the FC and the efficiency of the algorithm. The obtained results confirmed that the proposed SSA approach is superior and efficient than the other strategies. Highlights • SSA is used for optimally distribute the load demand among different components of hybrid power system. • The objective function is maximization of the energy supplied from both batteries and supercapacitors. • The proposed strategy is found superior to the other approaches with consuming 19.9549 gm of hydrogen. • The proposed strategy is found superior to the other approaches with efficiency of 81.22%. • The results confirm SSA has the superiority and validity than the other conventional strategies. [ABSTRACT FROM AUTHOR]

Over the past few decades, the demand for Data Center (DC) services has significantly increased due to the world's growing need for internet access, social networking, and data storage. Data Centers are among the most energy-intensive businesses, so optimizing IT operations in DC requires energy-efficient techniques. This paper presents AI based modeling strategies for effective energy management with a particular emphasis on DC's two most energy intensive systems (i.e., cooling and IT systems). This study addresses the issues of IT equipment performance degradation, inappropriate IT room thermal conditions, inefficient workload placement, and excessive energy waste. This research entails the application of machine learning for DC thermal classification, and deployment of deep learning models to predict resource utilization and energy consumption in DC. Furthermore, a comparative analysis is performed with existing relevant methods to demonstrate the effectiveness and accuracy of the proposed AI techniques. The findings of this study also provide evidence-based recommendations for DC efficient energy management. [ABSTRACT FROM AUTHOR]

Green building (GB) strategies are essential for mitigating energy wastage in the building sector, which accounts for nearly 40% of global energy consumption. However, due to the unpredictable nature of occupants' behavior and inadequate energy management, actual power consumption in GB can exceed intended values by up to 2.5 times. Building Automation Systems (BAS) are increasingly important in improving energy efficiency in GB. This study, therefore, aims to comprehensively review 143 articles published from 2008 to 2022 to explore the nexus between BAS and GB. This paper systematically illustrates 1) BAS applications throughout the lifecycle of GB; 2) BAS applications in supporting GB indoor human comfort, including thermal comfort, visual comfort, ventilation comfort, and acoustic comfort; 3) a research framework for reducing the energy performance gap in GB; 4) five BAS and GB integration methods for improving energy efficiency; and 5) limitations, challenges and future research directions in the BAS-GB domain. The findings reveal that current research in the BAS-GB domain is insufficient and predominantly concentrates on improving energy efficiency and occupant comfort. There are four challenges to achieving comprehensive integration of BAS and GB: uncertainties, long-term prediction and control, BAS-supported sustainability goals, and privacy and security. This study provides essential guidance on BAS implementation for GB development. The five BAS-GB integration approaches lay the groundwork for future research into achieving trade-off objectives between energy efficiency and occupant comfort. • Building automation systems (BAS) applications in green buildings (GB) have been comprehensively reviewed. • Model predictive control (MPC) integrating with BAS for improving energy efficiency of GB has been explained. • Four integration methods for achieving a trade-off objective have been discussed. • Challenges and trends in the BAS-GB domain have been highlighted. [ABSTRACT FROM AUTHOR]

Abstract: The concept of net zero energy and zero emission home has been a goal for those involved in the sustainable building industry. It is now becoming a reality in many parts of the world with an integrated approach of passive design, energy efficient appliances and local renewable generation. The paper describes the activities in Australia focussing on reducing energy use from the housing sector. It describes the regulatory approach of energy performance for housing which involves developing a house energy rating tool covering the building envelope as well as major energy consuming appliances. It also describes the implementation process which comprises gradually more stringent regulations coupled with financial inducements. The paper describes a world leading new Australian housing development where net zero energy homes are being built and enjoyed. It tells the story of how this outcome is being achieved starting with the planning process and developing environmental guidelines. The paper describes the innovations being used in order to achieve net zero energy homes and reports on energy monitoring results and feedback from householders. [Copyright &y& Elsevier]

Abstract: Turkey is one of a number of countries who still lack a national management standard for energy. Industrial energy consumption accounts for 42% of Turkey''s total energy consumption. With the help of a questionnaire and analytical framework, this paper investigates Industrial Energy Management Practice in Turkey and highlights significant bottlenecks and shortcomings of energy intensive industries in terms of energy management application. The survey was carried out as a multiple case study of the Turkish iron, steel, cement, paper, ceramics and textile industries. Outcomes of the questionnaire are evaluated according to the analytical framework which covers company characteristics, regulations, external relations of the companies and internal organizational conditions. After analyzing these elements on the basis of a minimum requirement list, it was found that only 22% of the surveyed companies actually practice corporate energy management in Turkey. The main barriers to proper energy management implementation were identified as lack of synergy between the stakeholders, the extent and scope of energy manager courses, and inadequate awareness of and lack of financial support for energy management activities. As a guideline to overcome present obstacles, a set of policy options are offered: strengthening and restructuring of legal and institutional frameworks, promotion of energy efficiency, education, training and capacity building and facilitating implementation of the international energy management standard ISO 50001. [Copyright &y& Elsevier]

Highlights • Overview of US Federal Energy Management (FEMP) lighting efficiency program. • Estimated total benefits of the 2017 lighting update from $33 to 217 million USD. • Estimated energy savings 0.33–4.76 TWh for this update. • Total benefits methodology with monetized full fuel cycle greenhouse gas emissions. • Presents the FEMP lighting categorization structure. Abstract Lighting consumes a great deal of electrical energy in buildings, and the U.S. federal government is the largest single energy consumer in the United States. Therefore, the way that the U.S. federal government manages its lighting energy use is of great importance. One important way that energy use is managed is through Federal Energy Management Program (FEMP) energy efficiency requirements. However, there is not much literature on how these requirements are set and how to value the resulting savings. This paper documents the methodology used to develop these for the 2017 FEMP lighting update. A methodology for calculating the total benefits is presented that goes beyond site energy saved, and includes full fuel cycle energy savings, energy cost savings, emissions offset and monetized emissions costs. The results show total benefits of $33 million USD including 475 kilotons of carbon dioxide (CO 2) offset under the low compliance scenario, $104 million USD including 1494 kilotons of CO 2 offset under the medium compliance scenario, and $217 million USD including 3124 kilotons of CO 2 offset under the high compliance scenario. [ABSTRACT FROM AUTHOR]

Machining workshop is a widely distributed manufacturing system that consumes massive energy in a low efficiency. In recent years, the increasing pressure from energy price and environmental directive have forced machinery manufacturers to address energy efficiency monitoring and management to improve economic benefits and environmental performance. However, due to the complicated energy flow and dynamic energy changes of the machining workshop, machinery manufacturers still lack an effective method to monitor and manage the energy efficiency in a practical manner. To this end, this paper proposes an energy efficiency monitoring and management system with the support of the newly emerging Internet of Things (IoT) technology. Firstly, the energy characteristics and energy efficiency indicators of the machining workshop are analyzed and defined. Then the framework of the IoT based energy efficiency monitoring and management system is proposed. The key approaches for energy efficiency monitoring and management are then illustrated. Finally, an industrial application is demonstrated to validate the effectiveness and benefits of the proposed system. With the application of the proposed system, potential opportunities for energy consumption decrement and energy efficiency improvement can be identified. Machinery manufacturers can easily reduce energy consumption and energy cost by managing the machining process. [ABSTRACT FROM AUTHOR]

Highlights • Analysis of possible energy savings in the German chemical industry. • Uncertainty analysis of costs and potentials of energy saving measures. • Evaluation of marginal cost curves of energy savings with uncertainties. • Determination of important model parameters using sensitivity analysis. Abstract In 2014, 19.3% of Germany’s industrial final energy consumption could be allocated to the chemical industry. Energy efficiency measures with focus on the chemical industry could thus contribute to reaching the German goal of reducing greenhouse gas emissions. To achieve this goal, energy planners and industries alike require an overview of the existing energy efficiency measures, their technical potential as well as the costs for realizing this potential. Energy efficiency opportunities are commonly presented in marginal cost curves, which rank these measures according to specific implementation costs. Existing analyses, however, do not take uncertainties in costs and potentials sufficiently into account. The aim of this paper is to create a marginal cost curve of energy efficiency measures for the chemical industry in Germany, while quantifying the uncertainties of the results and identifying the most influential input parameters. The identification of energy efficiency measures and the quantification of the associated technical potentials and costs were identified based on literature data and own assessments. Based on these findings a cost curve was created for the current technical potential. This potential was found to be 24.4 PJ per year, of which 23 PJ had negative lifetime costs. To investigate the uncertainties of these results, Monte Carlo simulations were performed to quantify the standard deviations of the implementation potential and costs. Furthermore, a sensitivity analysis, based on Morris Screening and linear regression, was conducted in order to identify the most influential model input parameters. With the applied approach, it was shown that uncertainties have a non-negligible impact on the final energy saving potential and costs, as well as the shape of marginal cost curves. The standard deviation of the energy saving potential was found to be 3.1 PJ. Furthermore, it is possible to systematically prioritise efforts in refining data. [ABSTRACT FROM AUTHOR]

Abstract The more efficient use of energy is a policy issue worldwide. Smart use of technology and management of information is becoming crucial for increasing energy efficiency. The 7th Framework Program of the European Union supported international research to fulfill the potential of Information and Communications Technologies for Energy Efficiency. Using Social Network Analysis, this paper analyzes this research network and explores the links and influences in the cross-country knowledge flows generated. The analysis identifies some important differences in the position that countries hold on the network. Results highlight the importance of countries acting as hubs and gatekeepers in the promotion of research and the improvement of energy efficiency. Highlights • The network of the 7th Framework Programme on Information and Communication Technologies for energy efficiency is studied. • Countries with a central position in the research network benefit from energy efficiency gains. • A fast access to knowledge does not guarantee efficiency improvements. • Climate-resilient countries tend to have more adaptation in terms of efficiency. [ABSTRACT FROM AUTHOR]

Abstract In this paper is the first attempt to structure energy management in buildings retrofitted by Energy Service Company to maintain designed energy performance level. The energy performance of multifamily buildings is investigated. More specifically, ex-post energy data of the buildings are analysed to see the dynamics of the energy performance. The analysis is based on a unique dataset that combines data from energy meters and energy monitoring system. The results indicate that multifamily buildings with proper energy management keep designed energy efficiency level meanwhile providing in the energy performance contract guaranteed indoor temperature in space heating season. [ABSTRACT FROM AUTHOR]

Abstract: The procedure of development of energy management system applied to an existing company (Serbian car producer “Zastava”) is shown in the paper. The aim of the paper is to provide a guideline for entrepreneurs in metal-working industry in implementing energy management system. First of all, paper includes: critical analysis of existing energy management system (energy matrix), principles of effective energy management organization (with energy manager and energy team in its structure) and energy management politics. Based on the results of energy auditing and performed technological and economical feasibility studies several energy saving measures related to different energy sources (steam, hot water, compressed air, electricity and water) were proposed, implemented and valuated. The proposed measures are not exclusively related to car assembly industry; they can be easily applied to other metal-working facilities with minor modifications. Such energy management system reduces energy costs and increase profitability of a factory. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the method and results of a survey of 27 of the 33 Australian universities teaching engineering education in late 2007, undertaken by The Natural Edge Project (hosted by Griffith University and the Australian National University) and supported by the National Framework for Energy Efficiency. This survey aimed to ascertain the extent of energy efficiency (EE) education, and to identify preferred methods to assist in increasing the extent to which EE education is embedded in engineering curriculum. In this paper the context for the survey is supported by a summary of the key results from a variety of surveys undertaken over the last decade internationally. The paper concludes that EE education across universities and engineering disciplines in Australia is currently highly variable and ad hoc. Based on the results of the survey; this paper highlights a number of preferred options to support educators to embed sustainability within engineering programs, and future opportunities for monitoring EE, within the context of engineering education for sustainable development (EESD). [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to conceptually analyze the cost-effectiveness of voluntary energy efficiency programs targeted at the industrial sector. In the paper a broad methodological framework for such an assessment is outlined, and this is applied empirically to discuss the potential cost-effectiveness of the ongoing Swedish energy efficiency program PFE. The focus lies on the presence of asymmetric information and bounded rationality, and the paper discusses how these factors may influence the cost-effectiveness of these types of programs. We also highlight the impact of the self-selection mechanism on the program''s cost-effectiveness. The results show that the presence of information inefficiencies and asymmetries represents one of the major motives for policy intervention in the industrial energy efficiency field, but the substitution of energy management systems for electricity taxes—such as that achieved in the Swedish PFE program—does not necessarily address these asymmetries entirely cost-effectively. First, the presence of firm-government information asymmetries implies that electricity taxes could do a better job in energy-intensive companies while an energy management system could be more effective in companies with a low energy-intensive production process due to the lack of prior experience of energy efficiency measures. The current set-up of PFE induces the reverse situation. Second, although firm-internal information asymmetries may well exist in the participating energy-intensive companies—implying that energy management systems could do a good job in detecting cost-effective measures—this is likely to be even more true for the non-participating companies (again due to their comparatively low accumulated knowledge in the energy efficiency field). It should however also be acknowledged that this analysis is static, and in the long-run voluntary energy efficiency programs may lead to significant learning impacts and—given the public good characteristics of this new knowledge—to important spillover effects. [Copyright &y& Elsevier]

Implementing machine-learning models in real applications is crucial to achieving intelligent building control and high energy efficiency. Over the past few decades, numerous studies have attempted to explore the application of machine-learning models to building energy efficiency. However, these studies have focused on analyzing the technical feasibility and superiority of machine learning algorithms for fitting building energy-related data and have not considered methods of implementing machine learning technology in building energy efficiency applications. Therefore, this review aims to summarize the current practical issues involved in applying machine-learning models to building energy efficiency by systematically analyzing existing research findings and limitations. The paper first reviews the application status of machine learning-based building energy efficiency research by analyzing the model implementation process and summarizing the main uses of the technology in the overall building energy management life cycle. The paper then elaborates on the causes of, influences on, and potential solutions for practical issues found in the implementation and promotion of machine learning-based building energy efficiency measures. Finally, this paper discusses valuable future machine learning-based building energy efficiency research directions with regard to technology opportunity discovery, data governance, feature engineering, generalizability test, technology diffusion, and knowledge sharing. This paper will provide building researchers and practitioners with a better understanding of the current application statuses of and potential research directions for machine learning models in building energy efficiency. • The paper is written from the perspectives of model application and promotion. • The paper summarizes current uses of ML-based building energy efficiency technology. • The paper discusses practical issues in applying ML to building energy efficiency. • The paper indicates future research directions for ML application and promotion. [ABSTRACT FROM AUTHOR]

With the help of several in-wheel motors, four-wheel independent-drive electric vehicle (4WIDEV) has tremendous potential to improve vehicle performance. Except for the theme of stability enhancement during the past two decades, energy saving topic becomes more attractive recently. However, it is difficult to achieve stability and economy performance simultaneously. Given the commonly used control method of rigid yaw rate tracking may limit the energy saving potential of 4WIDEV in cornering. Therefore, be different from previous studies which often focus on a sole target, in this paper, a driving energy management strategy for 4WIDEV based on multi-objective online optimization of four-wheel torque distribution is proposed in this paper, which includes weighted yaw rate tracking error into its parameterized control objectives besides electric drive system efficiency, tire slip losses and wheel torque ripple. Meanwhile, for better coordination of stability and economy in different situation, fuzzy logic control is adopted to dynamically adjust the weight of the control objective indexes of the proposed multi-objective online optimization function. Then, a particle swarm optimization algorithm is adopted to solve the optimization function. Finally, the proposed strategy for energy-efficient driving is verified based on the built Simulink and CarSim co-simulation model. The results show that the proposed four-wheel torque distribution strategy based on the multi-objective online optimization is more effective than average distribution strategy and offline optimization strategy. [Display omitted] • Propose a driving energy management strategy based on multi-objective online optimization. • Integrate yaw rate error, in-wheel motor efficiency, and tire slip into optimization function. • Design a weight coefficient fuzzy controller to better coordinate the stability and economy. [ABSTRACT FROM AUTHOR]

In recent years, smart cities have emerged with energy conservation systems for managing energy in cities as well as buildings. Although many studies on energy conservation systems of smart homes have already been conducted, energy management at the city level is still a challenge due to the various building types and complex infrastructure. Therefore, this paper investigated the research themes on smart homes and cities through a quantitative review and identified barriers to the progression of smart homes to sustainable smart cities through a qualitative review. Based on the results of the holistic framework of each domain (smart home and city) and the techno-functional barriers, this study suggests that the following innovative solutions be suitably applied to advanced energy conservation systems in sustainable smart cities: (i) construction of infrastructure for advanced energy conservation systems, and (ii) adoption of a new strategy for energy trading in distributed energy systems. Especially, to reflect consumer behavior and energy in sustainable smart cities, the following responses to future research challenges according to the "bottom-up approach (smart home level to smart city level)" are proposed: (i) development of real-time energy monitoring, diagnostics and controlling technologies; (ii) application of intelligent energy management technologies; and (iii) implementation of integrated energy network technologies at the city level. This paper is expected to play a leading role as a knowledge-based systematic guide for future research on the implementation of energy conservation systems in sustainable smart cities. Image 1 • In terms of energy technology, a connection between smart home and city is lacked. • Through quantitative review, research themes on smart home and city are identified. • Through qualitative review, barriers of progression to smart cities identified. • Bottom-up approach for energy conservation system in smart city is proposed. [ABSTRACT FROM AUTHOR]

This paper defines the diverging interests of multiple actors in typically achieving and advancing energy efficiency. Gaps in the literature include the short elaboration of energy efficiency factors and conditions and the lack of information about developed and developing countries and thorough consideration of their specific features, impeding the development and implementation of detailed energy efficiency management strategies. It is against this backdrop that this paper seeks to identify the essence of energy efficiency and developing specific recommendations for achieving the most beneficial effect and well-balanced energy efficiency on the global scale, within the achievements of international goals in sustainable development. The paper exposes the critical factors of energy efficiency, accurately models the best energy efficiency characteristics, and settles the best energy consumption structure. The methodological purpose is to find different energy efficiency scenarios and disentangle fundamental differences in energy efficiency in developed and developing countries. The paper indicates that the reasonable likelihood of noteworthy achievement results in energy efficiency with insignificant mid-term changes. We give recommendations about well-balanced energy efficiency on the global scale and global progress in sustainable development. • This paper disentangles key differences in energy efficiency in developed and developing countries. • We identify the essence of energy efficiency and develop recommendations for achieving balanced energy efficiency globally. • The paper models the best energy efficiency characteristics and settles the best energy consumption structure. • We give recommendations about well-balanced energy efficiency in sustainable development. • The study shows energy consumption optimization in terms of GDP structure, energy consumption, and sustainability. [ABSTRACT FROM AUTHOR]

Interoperability issues on networks of heterogeneous devices create a relevant and complex challenge. The Word Wide Web Consortium (W3C) develops a series of specifications, called the Web of Things (WoT), to address this challenge. WoT is focused on integrating smart things into the application layer of a system using on Web technologies. Thus, WoT is expected to have a great impact on the smart retrofit of buildings once data from existing and future planned systems are able to be gathered and analysed seamlessly. In this paper, we (i) analyse the application of the W3C WoT on the core of the Building Energy Management System (BEMS) to enable the universal integration of both private and public systems; (ii) propose a novel architecture for the BEMS based on the W3C specifications; and (iii) present a real-world application based on this architecture. The application covered in this paper allows data gathering from sensors with standard commands, enabling data analysis with a simple collecting process. By applying these specifications, smart building retrofit can be benefited from: (i) the ability to merge and scale different systems and devices; (ii) the easiness to access data; (iii) the reduction of development and maintenance costs; and (iv) offering standard interfaces to the BEMS. • Interoperability issues on networks of heterogeneous devices create a complex challenge. • Web of Things increases interoperability among Internet of Things platforms. • Smart retrofit of buildings can be foster with the adoption of Web of Things approaches. • Time and cost are saved when the smart retrofit of buildings adopts Web of Things. • The ability to scale building energy management systems is improved using Web of Things. [ABSTRACT FROM AUTHOR]

Water-energy nexus plays a vital role to re-phase, plan and develop an urban region for the present and future demands. This phenomenon is complex because of many inter-connected variables of technical and non-technical contexts. This paper reviewed the scope, aims, and approaches from the perspective of water-energy nexus studies in urban water system. The aim is to provide insights to minimize water-energy consumption and achieve maximum efficiency in urban water system management. A systematic review methodology was used to study 94 research papers, selected from relevant literature through the lens of water-energy nexus and energy efficiency. This research focuses on methodologies, models and affecting factors of water-energy nexus in different urban water system. The finding shows that most of studies used different frameworks and optimization approaches to achieve maximum energy efficiency. Integral Theory Framework (ITF) was used to find the inter-connectivity among existent variables of water-energy nexus for future demands. The ITF covers all aspects of water-energy nexus, not only for technological assessments, but also for other variables; technological, environmental, socio-economical, and geographical dimensions. It was concluded that water and energy management have a big gap between policy and implementation. A systematic understanding of relationship and interactions between water and energy was needed not only for researchers, but the managers as well. This study recommended that optimum water-energy efficiency is attainable only when almost all variables and integral sections of water-energy nexus are integrated according to the existent conditions, which are different from locality to locality and system to system. Image 1 • A systematic review on water-energy nexus was conducted in urban water systems (UWS). • Efficiency control measures were proposed to describe the water-energy nexus. • Integral Theory Framework was used to frame the inter-connectivity of existent variables in WEN for future demands. • Maximum energy efficiency can be achieved if all variables are integrated with water-energy nexus. [ABSTRACT FROM AUTHOR]

With the growing urbanization and environmental concerns over buildings' energy consumption and carbon footprint, the demand for energy-efficient building design is greater than ever. This paper addresses these concerns by presenting a novel method for estimating and optimising the thermal load (i.e. total energy load for heating and cooling) of a building within a real environment, provided by high-resolution LiDAR data, while considering long-term climatological parameters, estimated direct and anisotropic diffuse irradiance, shadowing from surroundings, and terrain topography. In the optimisation part of the method, the building's design is optimised regarding the estimated thermal load. The estimation was validated with the well-established EnergyPlus software. In experiments, a rectangular building's design was optimised on a flat and urban dataset. The effect of a building's design parameters on thermal load was inspected as well. On average, the proposed method improved a building's net heat gain by over 103 kWh/m 2 and reduced its thermal load by 234.18 kWh/m 2 when compared with the initial building design. [ABSTRACT FROM AUTHOR]

This paper presents the methodological approach and findings of a simulation study of advanced energy conservation, generation and management technologies applied to two case study dwellings in the UK, so as to achieve net zero energy (NZE) target that includes a reduction of net regulated energy (HVAC) to 0 kWh/m 2 per year and energy generation of at least 50 kWh/m 2 per year. The performance of the dwellings are also tested for a change in energy use and risk of summertime overheating in future climates in the short (2030s), medium (2050s) and long term (2080s). The findings indicate that to meet the NZE targets, regulated loads need to reduce by about half (over current Building Regulations) with community (rather than building-level) renewables playing a major role. It is also found that the NZE targets, given current regulations, are not particularly difficult to achieve in design with regard to energy efficiency but are highly expectant with regard to renewable energy. Further, summertime overheating is projected to be a risk for net zero energy dwellings by the 2050s. Despite this the regulated energy use of both dwellings is projected to reduce by 11 kWh/m 2 /yr, accompanied by a small increase in renewable generation (2 kWh/m 2 /yr). [ABSTRACT FROM AUTHOR]

Measuring and targeting energy performances of manufacturing plants and their subsystems is the first, critical step to understand their energy behavior, to identify energy management opportunities and to evaluate energy savings. In addition, this activity has become always more important in the last decades, as Energy Management Systems and energy management practices based on continuous improvement and people engagement have been largely adopted, requiring the introduction of new energy consumption control systems capable to identify the standard operative energy behavior and performance of systems and machines and their energy baseline, to point out contingent deviations of energy performances from the baseline and to identify possible causes and clearly attribute responsibilities of such deviations. In this context, several attempts have been presented in literature trying to identify methods to create Energy Performance Indicators so as to satisfy these requirements, but most of them are focused either on a physical or on economical definition of the problem (and therefore either on a process/appliance level or on an aggregated level), neglecting the necessity to develop an integrated system to combine those two approaches to the problem and deliver appropriate information to each hierarchical level and function of the company. In addition, even when these indicators are identified, a proper system to monitor and control their evolution over time is neglected. In this paper, a novel methodology to manage Energy Performance by developing, analyzing and maintaining Energy Performance Indicators in manufacturing plants is presented, which has been created to fill this gap and taking into account ISO 50001:2011 and ISO 50006:2014 requirements, in order to be as general and widely applicable as possible. The proposed methodology allows an immediate identification of manufacturing plant's energy performance deviations through Energy Performance Indicators monitoring over time and an easy association of causes and responsibilities to such deviations, in order to allow companies to rapidly react defining action plans and resetting targets if necessary; it has also been validated through an industrial case study, which will eventually be presented. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k -Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users. [ABSTRACT FROM AUTHOR]

An ontology-based approach for Eco-Industrial Park (EIP) knowledge management is proposed in this paper. The designed ontology in this study is formalized conceptualization of EIP. Based on such an ontological representation, a Knowledge-Based System (KBS) for EIP energy management named J-Park Simulator (JPS) is developed. By applying JPS to the solution of EIP waste heat utilization problem, the results of this study show that ontology is a powerful tool for knowledge management of complex systems such as EIP. The ontology-based approach can increase knowledge interoperability between different companies in EIP. The ontology-based approach can also allow intelligent decision making by using disparate data from remote databases, which implies the possibility of self-optimization without human intervention scenario of Internet of Things (IoT). It is shown through this study that KBS can bridge the communication gaps between different companies in EIP, sequentially more potential Industrial Symbiosis (IS) links can be established to improve the overall energy efficiency of the whole EIP. [ABSTRACT FROM AUTHOR]

Ubiquitous modernization of building energy and management systems brings tendencies for an effective integration of renewables in buildings. This article shows how solar energy can be in charge of energy savings within a building by integration of solar systems with Trombe wall. Numerical calculations of Trombe wall thermal resistance and its performances in comparison with a common wall are considered. Additionally, energy-efficiency gaining by a combination of PV modules and Trombe wall, using simulations by SAM (System Advisor Model) software is discussed. PV module parameters and their impact on its electricity production are observed and analyzed. The PV module parameters, analyzed in the paper are: the tilt angle α of PV modules, incident angle β of direct sunrays on PV modules, normal beam irradiation N and PV cells operating temperature. With bigger incident angle β of direct sunlight on PV modules, the normal beam irradiation is smaller, which brings less electricity production. The impact of PV module operating temperature can be neglected in comparison with the impact of tilt angle on PV module electricity production. The optimal tilt angle of PV modules for generating maximum annual electricity is around latitude angle. It varies with normal beam irradiation to achieve the maximum monthly electricity production. [ABSTRACT FROM AUTHOR]

Heating ventilation and air conditioning (HVAC) systems represent an important amount of the total energy use in office buildings, accounting for near 30%. Moreover, in countries affected by extreme climates HVAC systems’ contribution to energy demand increases up to 50%. Therefore, the automation of energy efficient strategies that act on the Building Energy Management System (BEMS) in order to improve building energy use becomes increasingly relevant. This paper delves into the devising of a novel HVAC optimization framework, coined as Next24h-Energy, which consists on a two-way communication system, an enhanced database management system and a set of machine learning algorithms based on random forest (RF) regression techniques mainly focused on providing an energy-efficient predictive control of the HVAC system. Therefore, the proposed framework achieves optimal HVAC ON/OFF and mechanical ventilation (MV) schedule operation that minimizes the energy consumption while keeps the building between a predefined indoor temperature margins. Simulation results assess the performance of the proposed Next 24 h-Energy framework at a real office building named Mikeletegi 1 (M1) in Donostia-San Sebastian (Spain) yielding to excellent results and significant energy savings by virtue of its capability of adapting the parameters that control the HVAC schedule in a daily basis without affecting user comfort conditions. Specifically, the energy reduction for the test period is estimated in 48% for the heating and 39% for the cooling consumption. [ABSTRACT FROM AUTHOR]

The concept of energy management (EnM) became a topical issue in industrial settings as a result of the energy crises that affected the global community in the 1970s. However, EnM was not implemented within industry with all its potential to improve energy security, raise the maturity level of EnM and increase sustainability. According to the results of previous empirical investigations, the expected interest in energy programs is not found and there is no clear understanding about program adoption criteria within an industry. Keeping in mind the adoption of energy investment through conformation with financial analysis and choosing the investments through contextual factors in the organization (e.g. organizational energy culture, power relationships, EnM system, expertise availability, managers’ mindset) together with characteristics of EnM program as two macro perspectives in energy efficiency literature, this paper aims to understand the main driving factors which lead organizations to either adopt or not adopt a particular program (always with respect to energy management). Moreover, it aims to express the impact of those driving forces of implementing a successful EnM program which could contribute to better understanding of suitable EnM configuration. The investigation has been conducted as a multiple case study involving 15 manufacturing companies of varying size and in different sectors located in Sweden. After analyzing the minimum required steps to establish EnM, assessing the adoption of practices according to their energy strategy, and through assessing EnM maturity level, we found a low level of risk (which arises from lack of certainty and awareness) and the program's alignment with the core business as prominent driving factors for all sizes which foster positive investment decision making through top management. On the contrary, complexity of industry (for large manufacturing companies) and access to capital (for small and medium-sized companies) are the main barriers to adopting those programs. [ABSTRACT FROM AUTHOR]