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The smart city model on multi-agent systems and the Internet of Things using a wireless sensor network is designed to improve the quality of life for citizens, increase resource efficiency, and reduce costs. This model enables the collection, analysis, and sharing of information by connecting and coordinating devices and systems within the smart city. In this model, intelligent agents act as sensors, and the smart gateway plays the role of a base station. The main goal of this model is to reduce energy consumption. To achieve this goal, intelligent agents are divided into clusters, with each cluster having a cluster head. The cluster head’s task is to collect and aggregate information from the intelligent agents within its cluster and send it to the smart gateway. In the proposed method, each intelligent agent selects a cluster in a distributed manner. An intelligent agent may choose another intelligent agent as its cluster head or select itself as a cluster head and directly send the data to the smart gateway. Each intelligent agent chooses the cluster head after calculating the importance level of neighboring intelligent agents. By using this model, cities can experience increased resource efficiency and cost reduction by leveraging innovative technologies. The proposed method has been implemented in different scenarios of smart cities, such as sparse and crowded smart cities with varying message sizes. In all simulations, the proposed method demonstrated good capabilities in optimizing energy consumption management. [ABSTRACT FROM AUTHOR]

The escalation of energy prices and the pressing environmental concerns associated with excessive energy consumption have compelled consumers to adopt a more optimal approach towards energy usage and an advanced infrastructure such as smart grids. Blockchain technology significantly improves energy management by creating supply chain resiliency in a distributed smart grid. This study proposes a blockchain-based decision-making framework with a dynamic energy pricing model to manage energy distributions, particularly during an energy crisis. Empirical data from U.S. consumers are employed to show the applicability of the proposed model. We include price elasticity to address changes in energy market prices. Findings revealed that the proposed framework reduces total energy costs and performs better when a disruption has occurred. This study provides a post hoc analysis in which four machine learning algorithms are used to predict energy consumption. Results suggest that the autoregressive integrated moving average (ARIMA) algorithm has the highest accuracy compared to other algorithms.

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The plug-in hybrid electric vehicle (PHEV) is a new type of energy vehicle that has extremely high demands for vehicle energy consumption management. This study aims to achieve refined energy management of PHEVs, while avoiding high-energy consumption associated with independent control of the thermal management system. For this purpose, a reliable co-simulation platform based on a particular PHEV energy and thermal management system architecture is developed. The platform aims to optimize engine fuel consumption and ensure the stability of the coolant temperature. This study introduces the NSGA-II genetic algorithm to the field of vehicle R&D to collaboratively control the rotary speed of the pump and fan, which highly accelerating the vehicle development cycle. The results show that the optimized algorithm for engine thermal management system efficiently improved fuel economy under dynamics driving conditions. Under SFTP-US06 conditions, the fuel consumption of the PHEV reduced by 0.46L/100 km, resulting in a fuel-saving rate of 5.67%, and the average power of low-voltage accessories was reduced by 81.81%.The approach used in this research can increase the accuracy of the virtual calibration and help shorten the automotive development cycle.

Managing energy consumption in smart buildings has become an increasingly important challenge. Efficient energy management can have a positive impact on both micro and macroeconomics. Moreover, it is essential to ensure that the comfort of smart building residents is maintained at an acceptable level. Optimization algorithms can be used to achieve user convenience while minimizing energy consumption. In this study, we propose an optimization approach that utilizes an agent-based architecture. This architecture comprises intelligent agents that communicate with each other via message exchange in a network structure consisting of three layers: (1) The switch layer monitors user preferences and comfort levels. (2) The coordination layer includes a coordinating agent that determines the optimal timing for electrical appliances to minimize electricity consumption costs and maximize user comfort. (3) The execution layer contains performer agents. Our focus in this research is on the coordination layer with the aim of reducing energy consumption costs and peak average rates, while increasing user comfort to the highest possible level. However, this optimization problem is highly complex due to the large number of electrical devices and their capabilities. To address this, we propose a hybrid method based on genetic and bat algorithms. We evaluated its performance based on objective functions and compared it with recent research on SmartHome and CU-Bems datasets. Our results demonstrate an improvement in performance

[Introduction] With the rapid expansion of the city, underground municipal engineering construction has been widely used. Underground municipal engineering has special requirements such as complex construction technology, wide space volume and installation limitation of process equipment. [Method] According to the above characteristics, combining with the relevant specification requirements and engineering examples, this paper expounded the current underground municipal engineering design of power supply and distribution system and automatic control design. [Result] The design ideas in load grade determination, energy saving optimization design, intelligent control, energy consumption management and other aspects are put forward, and some ideas and suggestions in energy saving, new energy are also put forward. [Conclusion] The main points of electrical design of underground municipal engineering are discussed and summarized, which can provide some reference for similar design.

Copyright of Science Technology & Industry is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is devoted to developing an integrated model to minimise energy consumption while maintaining desired productivity in Bernoulli serial lines with unreliable machines and finite buffers. For small systems, such as three- and four-machine lines with small buffers, exact analysis to optimally allocate production capacity is introduced. For medium size systems (e.g. three- and four-machine lines with larger buffers, or five-machine lines with small buffers), an aggregation procedure to evaluate line production rate is introduced. Using it, optimal allocation of machine efficiency is searched to minimise energy consumption. Insights and allocation principles are obtained through the analyses. Finally, for larger systems, a fast and accurate heuristic algorithm is presented and validated through extensive numerical experiments to obtain optimal allocation of production capacity to minimise energy consumption while maintaining desired productivity. [ABSTRACT FROM AUTHOR]

One of the main reasons of environmental pollution is energy consumption in buildings. Today, the use of renewable energy sources is increasing dramatically. Among these sources, solar energy has favorable costs for various applications. This study examined a commercial building in a hot and humid climate. The findings showed that choosing the optimal angle of solar panels with the goal of optimized energy consumption would yield reduced costs and less environmental pollutants with the least cost and maximum energy absorption. In this study, to calculate the energy requirements of the building, DesignBuilder software was used. To study the solar angles and estimate the energy produced by the solar panels, Polysun software was used after simulating the building energy. Energy simulation results showed that the whole building energy consumption was 26604 kWh/year. Finally, the evaluation results of solar panels showed that the energy produced by photovoltaic modules at an optimal angle of 31° would be equal to 26978 kWh/year, which is more than the energy required by the building. This system can prevent 14471 kg of carbon dioxide emissions annually. Sustainable energy criteria showed that for the studied building, photovoltaic modules could be used in energy production to reach a zero-energy system connected to the grid with an annual energy balance.

The proper pattern of electric energy management on the part of consumers is a key element of the system enabling its effective use. This pattern can be developed by providing consumers with information obtained from non-invasive load monitoring. Improvement electric energy management can be achieved by turning off unnecessary appliances and by shifting some of them to off-peak hours. A rational benefit for consumers is the reduction of electricity consumption costs. In this paper, energy disaggregation was abandoned by introducing virtual electrical devices and by using optimal selection of explanatory data obtained from high-frequency measurements. The concept of virtuality results from the simultaneous operation of many physical devices, and especially for which it is difficult to distinguish the state of load, e.g., small, medium or large. The explanatory data was selected using the integral information capacity indicator of individual features. The determination of this indicator is based on the correlations between the explanatory variables, the explained variable and the mutual correlations between the explanatory variables. The parameters selected in this way constitute input data for various artificial neural network architectures identifying the electrical appliances. The obtained research results are very promising and confirm the validity of the approach used. [ABSTRACT FROM AUTHOR]

Using nonintegrated multiple coils in an electromagnet is a way to manage energy and reduce maintenance costs. In order to model and analyze the lifting force and the magnetic field in different zone of the electromagnet, an exact magnetic equivalent circuit (MEC) is required. In this paper, an accurate MEC to analyze dual‐coil electromagnets is presented. MATLAB and ANSYS MAXWELL software are used to analyze the proposed MEC and simulate the magnetic field and the lifting force, respectively. In multi‐coil electromagnets, if a coil burns out, the electromagnet will still be able to lift the loads with a percentage of the nominal load weight. Also, it is only necessary to replace the burned coil, which reduces the maintenance costs of the electromagnet. Flexibility in winding excitation is another advantage of using multiple coils in electromagnets. In this case, one or more coils can be excited according to the weight of the load. This feature not only increases the lifetime of the coils but also reduces the energy consumption of the electromagnet and no need to use expensive power electronic converters to continuously control the current of coils. To evaluate the proposed MEC, the laboratory platform of an electromagnet was made to lift a load of 100 kg in the air gap of 2 mm and tested in two modes, single‐coil and dual‐coil. The analytical, simulation, and experimental results have very good agreement, indicating the high accuracy of the proposed MEC. [ABSTRACT FROM AUTHOR]

Historic residences often suffer from low energy efficiency due to inadequate management and obsolete infrastructure. To ensure these buildings operate in an environmentally friendly manner, intelligent energy control and management are necessary. Accurate indoor user positioning is fundamental to realizing occupant-centric controls. However, historic dwellings, with architectural heritage preservation requirements and diverse operational modes, present multiple challenges in terms of occupant monitoring and energy management. This research proposes a BLE positioning system based on the SAE-CNN algorithm to realize the analysis and prediction of occupant locations in a cost-effective and minimally-intrusive manner. The process begins by dividing spaces based on the mapped BIM model and cluster analysis. The RSSI maps of wireless signals collected from the Bluetooth beacons in each space are then preprocessed by Gaussian filter and sliding window techniques. Lastly, a machine learning algorithm refines the prediction model, which is integrated into a digital twin platform designed for energy consumption management. The system was implemented in a traditional Chinese dwelling in Wufu town, Fujian Province. The results are promising, showing that the positioning accuracy reached 1.5–2 m (95 % confidence interval), while the platform effectively presented the real-time positioning results as well as realized occupant-centric energy management. Moreover, the collected location information shed light on occupants' spatial usage habits, offering valuable insights for devising decarbonization retrofit strategies. • A novel indoor positioning system based on several digital technologies is proposed. • SAE-CNN optimization algorithm is applied to improve positioning accuracy. • Locating results are integrated by a platform to realize occupant-centric control. • This system helps historic dwellings manage energy in a low-intervention manner. [ABSTRACT FROM AUTHOR]

Information Centric Networks (ICNs) have been considered one of the most promising candidates to overcome the disadvantages of host-centric architectures when applied to IoT networks, having the potential to address the challenges of a smart city. One of the foundations of a smart city is its sensory capacity, which is obtained through devices associated with the IoT concept. The more sensors spread out, the greater the ability to sense the city. However, such a scale demands high energy requirements and an effective improvement in the energy management is unavoidable. To improve the energy management, we are proposing an efficient forwarding scheme in energy-constrained wireless ICNs. To achieve this goal, we consider the type of devices, their internal energy and the network context, among other parameters. The proposed forwarding strategy extends and adapts concepts of ICNs, by means of packet domain analysis, neighbourhood evaluation and node sleeping and waking strategies. The proposed solution takes advantage of the neighbourhood to be aware of the moments to listen and forward packets in order to consistently address mobility, improving the quality of content delivery. The evaluation is performed by simulation with real datasets of urban mobility, one from the lagoon of “Ria de Aveiro” and the other from a vehicular network in the city of Porto. The results show that the proposed forwarding scheme resulted in significant improvements in network content availability, in the overall energy saving and, consequently, in the network lifetime.

Joint energy consumption and trading management is still a major challenge in smart (micro) grids. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. Here, an inclusive formulation for energy management and trading of a micro/nano-grid (M/NG) is proposed in this article. Subsequently, a holistic solution to jointly optimizing the internal energy consumption management and external local energy trading for a smart grid including several M/NGs is provided. As the problem is computationally intractable, the proposed approach involves three hierarchical stages. First, a game-theoretic online stochastic energy management model is provided with a reinforcement learning solution by which the M/NGs can schedule their power consumptions. Second, an effective incentive-compatible double-auction is formulated by which the M/NGs can directly trade with each other. Third, the central controller develops an optimal power allocation program to reduce the power transmission loss and the destructive effects of local energy trading. The simulation results validate the efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

Considering the increasing rate of electrical energy usage, this energy has become one of the most important resources for human life. So all countries are seeking access to reliable and planned energy resource. Regarding the non-renewability of fossil fuel resources, especially oil and gas, the issue of replacing these types of energy with renewable energy has been considered for decades. Saving and optimal use of electrical energy in important applications such as residential and commercial buildings is critical. One of the most important factors for planning power consumption and optimizing it is accurate forecasting for next hours' power consumption of residential and commercial buildings. In this paper, first, the data sets of several residential buildings are analyzed using parallel wavelet converters. Then, using an optimal estimator model of the convolutional neural network, the short-term load of the building is estimated. The obtained results show that the proposed method has improved the prediction error about 70, 69 and 73 percent for ARIMA, SVR, and LSTM methods, respectively. [ABSTRACT FROM AUTHOR]

This paper studies energy consumption management of seawater Reverse Osmosis (RO) desalination plants to maintain and enhance the Voltage Stability (VS) of Power Systems (PS) with Photovoltaic (PV) plant integration. We proposed a voltage-based management algorithm to determine the maximum power consumption for RO plants. The algorithm uses power flow study to determine the RO plant power consumption allowed within the voltage-permissible limits, considering the RO process constraints in order to maintain the desired fresh water supply. Three cases were studied for the proposed RO plant: typical operation with constant power consumption, controlled operation using ON/OFF scheduling of the High-Pressure Pumps (HPPs) and controlled operation using Variable Frequency Drive (VFD) control. A modified IEEE 30-bus system with a variable load was used as a case study with integration of three PV plants of 75 MWp total power capacity. The adopted 33.33 MW RO plant has a maximum capacity of 200,000 m3/day of fresh water production. The results reveal that while typical operation of RO plants can lead to voltage violation, applying the proposed load management algorithm can maintain the vs. of the PS. The total transmission power loss and power lines loading were also reduced. However, the study shows that applying VFD control is better than using ON/OFF control because the latter involves frequent starting up/shutting down the RO trains, which consequently requires flushing and cleaning procedures. Moreover, the specific energy consumption (SEC) and RO plant recover ratio decreases proportionally to the VFD output. Furthermore, the power consumption of the RO plant was optimized using the PSO technique to avoid unnecessary restriction of RO plant operation and water shortage likelihood.

Software defined Wireless Sensor Networks (SDWSN) decouples the control plane from the data plane, thus moving the control logic from the sensor node to a central controller. SDWSN is a new paradigm that promise to simplify the network management of traditional Wireless Sensor Networks (WSN). This paper presents a reviews of SDWSN architectures, with special emphasis on the development of the data and control plans and the functionalities provided by these architectures. The development of this technology is still in the initial phase and although it brings benefits to solve the problems of the WSN, the inherent challenges of the WSN characteristics must be overcome. [ABSTRACT FROM AUTHOR]

Confronted with stringent goals for greenhouse gas emissions reductions, germany is trying to find the right balance of energy sources and efficiency measures to meet electricity demand. Nuclear may or may not be part of the mix. [ABSTRACT FROM AUTHOR]

Abstract The increasing electricity demand in the residential sector creates growing pressure on energy supply. The price-based demand response has been considered the most effective scheme to match supply and demand in residential sector. This paper established a multi-agent system framework to simulate the various types of energy demands in a multi-occupant household under the price-based demand response scheme. The results showed that the total electricity consumption and related costs could be reduced by 7% and 34%, which amount to 3.42 kWh and 4.63 RMB, without interruption to the household indoor comfort or degradation of their living quality. The different levels of electricity price sensitivity are responsible for 1.97 kWh electricity consumption curtailment and 4.30 RMB cost curtailment difference in a single day. Among the various types of loads, the shiftable loads have the largest price-based demand response potential, while the biggest contribution to energy saving is made by the sheddable loads. Cost savings are mainly delivered by the shiftable loads, followed by the sheddable loads and on-demand loads. In addition, EVs represent huge potential of load shifting and a large pool of energy storage, given the availability of the technique vehicle to grid. The multi-agent system model provides a generic framework for planning, simulating and optimizing complicated energy systems, which could help policy makers, power generators and utility managers to effectively manage the energy consumption in urban cities. Graphical abstract Image Highlights • A generic MAS framework was developed for home energy management. • The shiftable loads have the largest potential of PBDR and cost saving. • The sheddable loads make the biggest contribution to energy saving. • The load categorization is necessary and useful for implementing PBDR. [ABSTRACT FROM AUTHOR]

The optimal control of the heating, ventilation, and air-conditioning (HVAC) system in buildings has a significant energy saving potential and therefore is of a great practical interest. An event-based HVAC control adjusts control actions when certain events occur, which may be faster and more scalable than state-based or time-driven control methods. However, events may capture either local or global changes in the rooms. The choice of events is a tradeoff between the computational efficiency and the control performance. This challenging problem remains open. We consider this as an important problem in this paper and make three major contributions. First, we define local and global events for the HVAC control problem. The complexity of these event-based control policies is defined. Second, based on hypothesis testing, we develop a method to select events that capture a sufficient state information and with a relatively small event space. Third, we demonstrate the performance of this method on two groups of examples, including one group of small-scale problems for the proof of concept and the other group of large-scale problems in the HVAC control. It is shown that our method outperforms the Levin search, which is a traditional complexity-based search method and finds event-based HVAC control policies with a good performance. Note to Practitioners—When there are multiple rooms in a building, the HVAC control may achieve a significant energy saving and an indoor comfort satisfaction in the same time through exploring the coupling among the rooms. By appropriately defining the events, the size of the event space is usually much smaller than the state space. Therefore, an event-based control is more scalable and preferred in practice. Local events capture the state changes of rooms in a small neighborhood, which leads to a small event space but limited information. Global events capture the state changes of rooms in a large neighborhood, which leads to more information but a large event space. It remains open how to select events in large-scale HVAC control problems, especially when the computing budget is limited. In this paper, we define the complexity of an event-based control policy by the number of neighboring rooms considered. We develop a method based on hypothesis testing to select events with a proper complexity in order to achieve a good system performance. The performance of this method is demonstrated on an HVAC control problem. [ABSTRACT FROM AUTHOR]

This paper proposes a resilience-oriented design (ROD) technique to protect distribution grids against high-impact but low-probability extreme weather events. The problem is formulated as a two-stage stochastic mixed integer problem. The first stage is to make ROD decisions, i.e., hardening existing distribution lines and deploying ROD resources such as back-up distributed generators and automatic switches. The second stage evaluates the system operation cost during a realized extreme weather event and repair cost after the event. A novel modeling strategy is proposed to deal with the decision-dependent uncertainty of distribution line damage status, which is affected by the first-stage hardening decisions. As both stages have binary variables, a modified and computationally efficient progressive hedging algorithm with scenario bundling is introduced. The algorithm performance is evaluated by calculating lower bounds of solutions. The proposed model and algorithms are demonstrated on 34-bus and 123-bus test feeders. [ABSTRACT FROM AUTHOR]

In this paper, a multiagent-based model is used to study distributed energy management in a microgrid (MG). The suppliers and consumers of electricity are modeled as autonomous agents, capable of making local decisions in order to maximize their own profit in a multiagent environment. For every supplier, a lack of information about customers and other suppliers creates challenges to optimal decision making in order to maximize its return. Similarly, customers face difficulty in scheduling their energy consumption without any information about suppliers and electricity prices. Additionally, there are several uncertainties involved in the nature of MGs due to variability in renewable generation output power and continuous fluctuation of customers’ consumption. In order to prevail over these challenges, a reinforcement learning algorithm was developed to allow generation resources, distributed storages, and customers to develop optimal strategies for energy management and load scheduling without prior information about each other and the MG system. Case studies are provided to show how the overall performance of all entities converges as an emergent behavior to the Nash equilibrium, benefiting all agents. [ABSTRACT FROM AUTHOR]

Current demographic trends are increasing both the populations and population densities of major cities in the United States, creating increasing strain on both the aging electrical distribution network and the environment. Government policy increasingly supports the renewal of critical infrastructure and the adoption of green energy alternatives, but the adoption of these alternatives has been slow because of the cost of retrofitting existing structures. It is proposed, to address these issues, to incorporate a trigeneration microgrid into a residential development from inception. These microgrids are based on combined cycle power generation and incorporate maximal renewable energy platforms. Case studies are presented for 13 cities and show that incorporation of these technologies into a development from inception not only reduces strain on the power grid, but also drastically reduces emissions and accelerates the time to repay investment as compared to same-sized developments, not including these technologies. [ABSTRACT FROM AUTHOR]

Goal-setting in conjunction with feedback on the degree of goal attainment can assist consumers in monitoring and, thus, reducing or stabilizing their in-home energy use. A field experiment compared the effects of daily electronic feedback with monthly external feedback and with self-monitoring of energy use. With daily feedback, a 12.3 percent reduction in natural gas use was achieved, more than in the other conditions and more than the stated 10 percent conservation goal. One year after the experiment, energy use remained reduced relative to the baseline in all groups; however, differences between the experimental conditions were no longer significant. [ABSTRACT FROM AUTHOR]

This paper introduces an intuitively appealing option for consumer policy on the energy crisis. Results of a consumer experiment indicate that life cycle cost can be communicated, and could produce energy-efficient purchases yielding savings of over four billion dollars per year by 1985. [ABSTRACT FROM AUTHOR]

Background: Since, energy consumption per square meter in hospitals is much higher than other types of service institutions; in this study, some actions performed toward optimizing energy consumption improvement projects based on the definition and prioritization in hospital. Materials and Methods: The necessity of optimizing energy consumption in hospitals were described according to some scales including the average consumption and cost of water, electricity and gas for bed days and active bed, and these indices calculated in the case. Then, improvement actions were identified using energy audit, interviews with hospital experts and conducted studies in hospitals. Next, projects with high importance were extracted regarding to impact on energy consumption indices, expert opinion, aggregation, feasibility, and limitations such as prerequisite, synchronicity and post-requisite. Finally, specific criteria were identified in three dimensions, i.e., factors affecting the level of energy consumption, trying to execute project and risk and the projects were prioritized using questionnaire and FAHP. Results: The study results revealed that energy consumption was higher than the world standards in the studied hospital; this confirmed the necessity of optimizing energy consumption and using energy management systems. The results of prioritization also showed the first four priorities. Conclusion: It seems necessary to save energy consumption through improvement projects implementation in the hospitals. Regarding the number and resources limitation, hospitals can choose to implement some low risk and payback period projects based on existing priority and budget annually.

Understanding and mastering the color change of wood during heat treatment is essential in the wood working industry because it saves time and reduces energy costs. An artificial neural network (ANN) was employed in this study to establish the relationship between the process parameters of heat treatment and the color change of wood. Three important parameters: temperature (180 °C, 190 °C, 200 °C, 210 °C, and 220 °C), treatment time (2 h, 4 h, 6 h, and 8 h), and wood species (larch and poplar) were considered as inputs to the neural network. There were four neurons in the hidden layer that were used, and an output layer as wood color. According to the results, the mean absolute percentage errors were determined as 0.53%, 0.65%, and 0.31% in the prediction of color change color (ΔE) values for training, validation, and testing data sets, respectively. Determination coefficients (R2) greater than 0.99 were obtained for all data sets with the proposed ANN models. These results showed that ANN models can be used successfully for predicting the color changes in wood during heat treatment. [ABSTRACT FROM AUTHOR]

The number of mobile users and the volume of big traffic data generated by user terminals are dramatically increased with diverse applications. Development in different economic and social sectors not only require higher network speed but also need low-cost on information exchanges. The market needs a complete set of solutions, which can provide a comprehensive management system for the operation and billing of a fine-grained data traffic system. This study addresses issues related to big traffic data reverse-charge for customers, which is a new type of consumption model that enables customers to save money with the cooperation of operators and online enterprises. We apply virtual private networking to identify apps, so as to help operators easily calculate the big traffic data of each app and reverse the related charges from the consumer to the relevant online enterprise. Online enterprises are willing to pay the expenses of some of their apps to enhance the user experience of customers exploring wireless services. To prove the popularity of our proposed system, we have experimentally employed the proposed reverse charge system, and participants’ responses to questionnaires indicate a win−win situation for customers as well as online enterprises. We hope that the proposed system will be implemented in practice, subject to customers, online enterprises, and operators agreeing to upgrade conventional charging infrastructures. [ABSTRACT FROM AUTHOR]

We consider a real-time demand response system in smart grid distribution to solve real-time pricing’s (RTP) total cost minimization problem. This problem has not yet to be discussed especially at the level of neighborhood area networks. While the available RTP-alike schemes are too stand-alone to solve all the problems systematically, this paper proposes a mathematical energy scheduling model for RTP demand response, and based on which, a distributed scheduling algorithm of energy consumption for total cost minimization is proposed. Simulations are conducted to find the minimum total cost under different sets of parameters. [ABSTRACT FROM AUTHOR]

The Smart Grid (SG) is an evolution of the electricity network that dynamically integrates the activities and energy control of power consumers, power generators, distribution systems, and devices connected to the grid (e.g., substations, transformers, and so on). The goal of the SG is to economically and efficiently deliver a sustainable, reliable, and secure electricity supply. Machine-to-Machine (M2M) technology is designed for automated data exchange between devices, and thus has applicability to SGs. With M2M technology, organizations track and manage assets; inventories; transportation fleets; oil and gas pipelines; mines; wide-spread infrastructure; natural phenomena such as weather conditions, crop production, forestry condition, and water flows; and, as noted, SGs. Wireless communication is a staple of M2M. These wireless technologies range from unlicensed local connectivity, to licensed 3G/4G/5G cellular, to Low Earth Orbit (LEO) satellites. All of these technologies are relevant to the SG. Utilities have started to gradually support M2M and Supervisory Control And Data Acquisition (SCADA) systems over satellite links. This article focuses on wireless and satellite-based M2M services, as applicable to the Smart Grid, including the use of Internet of Things (IoT), particularly for the transmission and distribution (T&D) space sector; some comparisons to wireline solutions are also discussed. [ABSTRACT FROM AUTHOR]

Statistical queuing models are popular to analyze a computer systems ability to process different types requests. A common strategy is to run stress tests by sending artificial requests to the system. The rate and sizes of the requests are varied to investigate the impact on the computer system. A challenge with such an approach is that we do not know if the artificial requests processes are realistic when the system is applied in a real setting. Motivated by this challenge, we develop a method to estimate the properties of the underlying request processes to the computer system when the system is used in a real setting. In particular we look at the problem of recovering the request patterns to a CPU processor. It turns out that this is a challenging statistical estimation problem since we do not observe the request process (rate and size of the requests) to the CPU directly, but only the average CPU usage in disjoint time intervals. In this paper we demonstrate that, quite astonishingly, we are able to recover the properties of the underlying request process (rate and sizes of the requests) by using specially constructed statistics of the observed CPU data and apply a recently developed statistical framework called Approximate Bayesian Computing. Further we apply the model to forecast future CPU consumption. Our results show that the model forecast future CPU consumption with less error than both the hidden Markov model (HMM) in (Hammer et al. 2016) and an ARIMA model. Another good property of the queue model is that we can forecast the instantaneous CPU consumption at any time point in the future, while the HMM in (Hammer et al. 2016) and time series models are limited to only forecasting the average CPU consumption in disjoint time intervals. [ABSTRACT FROM AUTHOR]

In the medium access control layer (MAC) of WSN, the scheduling mechanism of nodes based on the periodical listen/sleep is an effective way of saving node energy consumption. In the case of data transmission which is not affected reduces the nodes’ proportions, we improve the protocols based on asynchronous MAC. This paper discusses a protocol which is a low duty cycle energy-efficient MAC protocol for WSN and can be adaptively updated based on the prediction nodes’ wake-up time. We call it AP-MAC protocol. In AP-MAC protocol, the nodes will not wake up or send data in the same period, and they will wake up in random time according to the algorithm that has been set. In this case, the network can avoid the problem of collision, cross-talk, etc. caused by all the nodes’ wake-up in the same time, and save more energy. To ensure the reliable transmission of network data, the node which sends data will predict the wake-up time of receiving nodes and ensure the receiving nodes wake up timely and establish a connection with sending note. At the same time, we join several adaptive update mechanisms in the network according to the dynamic changes of it. The experimental results show that the improved protocol not only can save the network energy consumption by effectively reducing the overall duty cycle of the network nodes and improving the reliable transmission of data but also can improve the adaptability of the network. [ABSTRACT FROM AUTHOR]

Households play a crucial role in the transition of the energy system. They can make major contributions to sustainable development by adopting “green” innovations since such actions have a substantial impact on overall energy consumption. Theory suggests that innovations are first adopted by persons with specific dispositions, i.e., people with a high level of technical interest, relevant knowledge, and proenvironmental orientation. As an innovation spreads, early adopters yield social influences on others encouraging them to adopt, too. In theory, such effects should be found regardless of the nature of the innovation. We compared three green investments (residential insulation, solar thermal energy systems, and e-cars) at different diffusion stages in Germany. In three experiments, decision-makers in households were asked for their willingness to adopt the innovations; their dispositions and perceived social influences were measured in questionnaires. Social influences were found to affect all innovation adoptions while dispositions showed specific effects: technical interest was found to affect e-car and solar thermal energy adoptions; a proenvironmental value orientation only affected residential insulation. We conclude that it is worthwhile to shape policy measures to the innovation characteristics. However, social influences should always be taken in account. [ABSTRACT FROM AUTHOR]

In this paper, a model of homeowners’ energy efficiency retrofit choices is presented based on the database of a project implemented by the national energy efficiency retrofit scheme in Croatia during 2015 and 2016. The analysis was performed on the cleaned dataset of 4610 implemented projects in privately owned family houses. We have analyzed available data to answer how much money consumers are willing to pay for energy refurbishment and how this corresponds to household characteristics. We have developed linear regression models for estimating costs and energy savings of different measures and have further applied multinomial and nested discrete choice models on investment choices made by homeowners. The resulting models provide estimations of willingness to pay for energy efficiency refurbishment in private family houses in relation to income class the owners belong to. The results show that homeowners in all income classes have a similar level of willingness-to-pay for similar energy efficiency measures, with main differences being their capability to invest. With the current level of subsidies, only three of four observed measures show a significant level of free-riders. This suggests there could be a better allocation of public funds, where less money could be spent for subsidies to generate a similar amount of savings. Furthermore, the proposed model replicates existing experience and provides insight into inconsistencies which appear when the effectiveness of implemented measures is analyzed. [ABSTRACT FROM AUTHOR]

Intelligent systems in Wireless Sensor Networks (WSN) allow to eliminate duplicated sensed data and to efficiently route them to a central processing unit. Thus, augmenting WSN lifetime while keeping sensors connectivity and routing the sensed information through an optimized network structure. This work discusses a set of improved exact solution strategies that were effective to the design of a minimum energy consumption structure for the sensors data communication. We present an extended version of a primal-dual model for the minimum cost dominating tree (DT) problem, as well as valid inequalities to improve its linear relaxation. We discuss structural properties of dominating sets and trees used to conceive efficient cutting plane strategies. We adapt accordingly reduction techniques from dominating sets to the DT problem. The new solution techniques allow to handle to optimality challenging benchmark and new randomly generated DT instances with up to 100 nodes, which was not possible to achieve with state-of-the-art models for the problem. [ABSTRACT FROM AUTHOR]

The quantum of power consumption in wireless sensor nodes plays a vital role in power management since more number of functional elements are integrated in a smaller space and operated at very high frequencies. In addition, the variations in the power consumption pave the way for power analysis attacks in which the attacker gains control of the secret parameters involved in the cryptographic implementation embedded in the wireless sensor nodes. Hence, a strong countermeasure is required to provide adequate security in these systems. Traditional digital logic gates are used to build the circuits in wireless sensor nodes and the primary reason for its power consumption is the absence of reversibility property in those gates. These irreversible logic gates consume power as heat due to the loss of per bit information. In order to minimize the power consumption and in turn to circumvent the issues related to power analysis attacks, reversible logic gates can be used in wireless sensor nodes. This shifts the focus from power-hungry irreversible gates to potentially powerful circuits based on controllable quantum systems. Reversible logic gates theoretically consume zero power and have accurate quantum circuit model for practical realization such as quantum computers and implementations based on quantum dot cellular automata. One of the key components in wireless sensor nodes is the cryptographic algorithm implementation which is used to secure the information collected by the sensor nodes. In this work, a novel reversible gate design of 128-bit Advanced Encryption Standard (AES) cryptographic algorithm is presented. The complete structure of AES algorithm is designed by using combinational logic circuits and further they are mapped to reversible logic circuits. The proposed architectures make use of Toffoli family of reversible gates. The performance metrics such as gate count and quantum cost of the proposed designs are rigorously analyzed with respect to the existing designs and are properly tabulated. Our proposed reversible design of AES algorithm shows considerable improvements in the performance metrics when compared to existing designs. [ABSTRACT FROM AUTHOR]

When renovating an existing building, keeping the original window and installing added convex window is beneficial for the reduction of heating energy demand. This solution is free of the disposal and/or recycling of demolition waste. In the summer season as far as the transparent structures of the building are concerned, in general, heat protection has to be used. In the case of the added convex window, it can be provided by the external air curtain mode; the movable shading device is in the buffer zone. This is a well-protected place for it, so that it can be as simple and cheap as an inner shading device. In this article examinations of the efficiency of the external air curtain mode for the heat protection is presented. The summer overheating has been analysed in two experimental huts. It has been proven that simple shading device in added convex window tempers the summer overheating as efficiently as the combination of usual window and external shading device (which is exposed to heavy weather effects and is difficult to maintain). [ABSTRACT FROM AUTHOR]

Purpose Electric lighting accounts for a large share of energy consumption in commercial buildings. Utilization of daylight can significantly help to reduce the need for artificial lighting, increase workers productivity, customers’ satisfaction and consequently improve sales. However, excessive use of glazing and absence of lighting controls can contribute greatly to higher energy need for heating and cooling and cause undesired glare effects. Thus, optimizing the size, position and materials of external glazing, with the addition of deflectors and dynamic artificial lighting, can become key aspects in the design of sustainable low energy buildings. The purpose of this paper is to analyze daylight potential and energy performance of a hall-type commercial building, situated in the cold climate of Finland, by utilizing different combinations of skylights, windows and lighting controls.Design/methodology/approach The authors have used computer simulations to estimate daylight and energy performance of a single floor commercial building in relation to various combinations of skylights and windows with variable glazing materials, light deflectors and zonal lighting controls.Findings The results show that electric light energy saving potential ranges from a negligible 1.9 percent to a significant 58.6 percent in the case of glass skylights and wall windows using multi-zone lighting control. Total delivered energy ranges between increase of 1.5 and 21.2 percent in the cases with single zone lighting control and between decrease of 4.5 percent and increase of 4.5 percent in the cases with multi-zone control. The highest decrease in primary energy consumption was 2.2 percent for single zone and 17.6 percent for multi-zone lighting control. The research underlines the significant potential of electric light energy savings using daylighting strategies that, including the control of direct solar access for glare and internal gains, can be more than 50 percent.Originality/value This research combines accurate daylight and energy assessment for commercial hall buildings based in cold climate region with multiple design variations. The novelty of this work is the consideration of interior elements, shelves and deflectors, in the calculations. This is made possible through the combined use of validated simulation platforms for detailed annual daylighting and electric lighting calculation (Radiance and Daysim) and energy analysis (IDA-ICE, Equa Simulation AB). This method allows to obtain a reliable assessment of the potential of using natural light sources in buildings. [ABSTRACT FROM AUTHOR]

Many fields of modern science rely more and more on the immense computing power of supercomputers. Modern, multi-thousand node systems can consume megawatts of electrical energy in highly uneven manner, challenging the data center infrastructure, both power and cooling coils. The traditional way of managing the infrastructure makes each subsystem of a data center (e.g. cooling) independent from all other in the way it relies only on local sensors to manage the infrastructure. The erratic nature of computing in a large data center makes this approach suboptimal. In the paper we show that by challenging the traditional split between the infrastructure and the computing equipment, one can gain significant boost in energy efficiency of the entire ecosystem. A solution that predicts cooling power demand basing on the information from a supercomputer resource manager, and then sets up the parameters of the cooling loop, is presented along with potential benefits in terms of reduction of the power draw. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata is one of the recent new technologies at the nanoscale that can be a suitable replacement for CMOS technology. The circuits constructed in QCA technology have desirable features such as low power consumption, high speed and small size. These features can be more distinct in memory structures. In this paper, we design a new structure for content addressable memory cell in QCA. For this purpose, first, a unique gate is introduced for mask operation in QCA and then this gate is used to improve the performance of CAM. These structures are evaluated with QCADesigner simulator. [ABSTRACT FROM AUTHOR]

The continuous diffusion of information and communication technology (ICT) has exerted growing influences on the production process and energy use. This paper analyzes the major drivers behind changes in China's energy intensity with emphasis on ICT and production structure using a three-tier structural decomposition analysis (SDA) approach. This approach could thoroughly quantify the various effects of ICT on energy intensity and investigate the mechanisms of ICT input change through which it affects sectoral energy consumption. The main results indicate that: (a) production structure exerted a rising negative effect on China's energy intensity change from 2002 to 2012; (b) ICT contributed to a 4.54% increment in energy intensity, yet ICT input substitution was conducive to reduce energy use in production; (c) the ICT effects were more significant in the service sector and technology-intensive sectors. The study suggests that the Chinese government should formulate measures for adjusting production structure, promoting sustainable development of ICT and harmonizing inter-sectoral ICT input level. [ABSTRACT FROM AUTHOR]