Energy Efficiency Optimization Model Research Articles

Game theory-based energy efficiency optimization model for the Internet of Things

This paper conducts a game-theoretic based optimization study on the energy efficiency of the Internet of Things (IoT). For the problem that wireless sensor node information in the IoT requires the selection of a suitable backbone access point for energy efficiency optimization, this paper first establishes a mathematical model for the system-level energy optimization of the sensor node free-choice access point problem and then proposes a game model based on the concept of cooperation and the corresponding utility function, and after theoretical analysis. Among the new connections in the future, more than 30% are suitable for carrying by the cellular network, so the network challenges brought by mobile operators are huge. The paper then studied the current mainstream Internet of Things technology in the development of mobile networks—the​ application principles and key technologies of Narrow Band Internet of Things (NB-IoT), combined with the current wireless network optimization and maintenance work, and studied How does the Internet of Things become the focus of the industry and actively lay out and promote industrial development for operators, and how does NB-IoT move from a concept to small-scale commercial use, become the operator’s leading role in promoting the standard industry, and hope to become an industry leader It is proved that the best access point allocation scheme is the optimal equilibrium point of the proposed game. Then a non-correlated parallel learning algorithm is proposed, according to which the system can converge to the optimal equilibrium point with a very low probability after learning, which is the optimal solution of the proposed system energy efficiency optimization problem, and achieve the global optimal system energy efficiency. Compared with other models, our model has improved efficiency by about 12% and accuracy by about 8%, and it can be applied in practice.

Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud.

Energy Efficiency Evaluation and Optimization of Industrial Park Customers Based on PSR Model and Improved Grey-TOPSIS Method

Energy efficiency evaluation of industrial park customers is carried out to help customers develop optimization strategies and improve the energy efficiency level. The evaluation indexes are proposed based on pressure-state-response (PSR) model and the redundant indexes are selected by combining principal component analysis (PCA) and correlation analysis. Thus the dynamic energy efficiency index system is constructed. The weight of indexes is calculated by the entropy weight method (EWM). An energy efficiency evaluation model based on improved Grey-technique for order preference by similarity to ideal solution (TOPSIS) is proposed, which replaces the Euclidean distance by weighted grey correlation degree and can accurately evaluate customers’ energy efficiency level. An energy efficiency optimization model is proposed, which can maximize optimization benefits while improving energy efficiency. The validity of the evaluation and optimization models is verified by the case study of an industrial park.

Energy Efficiency Analysis and Optimization of Industrial Processes Based on a Novel Data Reconciliation

The energy management and energy efficiency optimization are of particularly significant for promoting the sustainable development of industrial processes. However, industrial raw data with uncertain, relevant and inaccuracy characteristics influence the reliability and accuracy of energy efficiency analysis and optimization modeling. Therefore, an energy efficiency analysis and optimization method based on a novel data reconciliation (DR) integrating Gaussian mixture model (GMM) and mutual information (MI) is put forward. First, the material flow information with multiple data characteristics corresponding operation modes is divided through the GMM. Moreover, the novel data reconciliation model integrated with critical variable and mutual information is established considering time-scale redundancy information in different modes, then the comprehensive data reconciliation result is evaluated by the hypothesis testing. Furthermore, the reconciled data is applied to analyze the exergy balance and built the energy efficiency optimization model with multi-objective for a case study of industrial evaporation process. Finally, simulation case and industrial application case are used to analyze and discuss, and the results show that the validity and applicability of the proposed approach are illustrated in energy saving potential which is about 14.81%.

Data-driven ship energy efficiency analysis and optimization model for route planning in ice-covered Arctic waters

As increasing numbers of merchant ships navigate in the Arctic waters, more energy efficient navigation in the Arctic is needed for both economic and environmental purposes. This paper aims to provide a comprehensive analysis of energy efficiency of ice-going ships. Firstly, a data-driven model based on neural network theory is developed to predict the energy efficiency of ships in the Arctic. Then, a route with optimum energy efficiency is presented, intended to cut costs and be as environmentally friendly as possible. Finally, a case study is carried out to analyze the performance of a case study ship sailing at various environmental conditions. The results indicate that when planning the route for such an Arctic ship, it would be shortsighted only to focus on distance rather than energy efficiency. Moreover, the model shows good agreement between the simulation and real-life navigation. It is expected that the construction of this multi-angle, energy efficiency optimization model could help to improve Arctic navigation.

Energy Efficient Resource Allocation for Wireless Power Transfer-Supported D2D Communication With Battery

Recently, researchers pay much attention to Wireless Power Transfer-supported Device-to-Device (D2D) Communication underlaying Cellular Network (WPT-DCCN), which has reliable and controllable energy resource, high energy and spectral efficiency. However, the Energy Efficiency (EE) of the whole WPT-DCCN, which combines the time allocation and the power control of all users (including base station, cellular and D2D user), is rarely studied. Consequently, this study establishes an EE optimization model of the whole WPT-DCCN. Facing the complexity of the modeled mathematical model, the EE problem is approximately converted to a convex one by a proposed Convex Approximation Scheme (CAS). Due to the fractional expression of the variables needed to be solved, it is hard to directly obtain an optimal solution for the converted convex problem through current convex optimization toolbox such as CVX. Thus, we skillfully utilize the Generalized Benders Decomposition (GBD) to obtain an optimal solution for the converted convex problem by analyzing the characteristics of the converted problem. Simulation results show that the proposed CAS can give a tight lower-bound solution of the original EE problem. Furthermore, CAS can help us to investigate some meaningful system features, for example the suitable energy conversion efficiency setting under different WPT-DCCN scenarios, which can give us a clear reference about the deployment of future WPT-DCCN.

Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors

Energy efficiency of inland ships is significantly influenced by navigational environment, including wind speed and direction as well as water depth and speed. The complexity of the inland navigational environment makes it rather difficult to determine the optimal speeds under different environmental conditions to achieve the best energy efficiency. Route division according to the characteristics of these environmental factors could provide a good solution for the optimization of ship engine speed under different navigational environments. In this paper, the distributed parallel k-means clustering algorithm is adopted to achieve an elaborate route division by analyzing the corresponding environmental factors based on a self-developed big data analytics platform. Subsequently, a ship energy efficiency optimization model considering multiple environmental factors is established through analyzing the energy transfer among hull, propeller and main engine. Then, decisions are made concerning the optimal engine speeds in different segments along the path. Finally, a case study on the Yangtze River is performed to validate the present optimization method. The results show that the proposed method can effectively reduce energy consumption and CO2 emissions of ships.

An Incentive-based Optimal Energy Consumption Scheduling Algorithm for Residential Users

Smart Grid is the most promising concept which is more reliable, flexible, controllable and environment friendly. Home energy management (HEM) system is an important part of the smart grid that provides a number of benefits to the end users such as savings in the electricity bill, reduction in peak demand and meeting the demand side requirements. Demand Response (DR) and Time-of-Use (ToU) pricing refer to programs which offer incentives to the end users who curtail their energy use during times of peak demand. This paper proposes an energy efficient optimization model based on Binary Particle Swarm Optimization (BPSO) for residential electricity consumers. The proposed model optimally schedules the electricity consumption of different household appliances in a dynamic pricing environment to benefice the user by minimizing electricity cost. Simulation results illustrate that the proposed method efficiently shifts the appliances operation time from high peak to low peak hours and leads to significant electricity bill saving.

A cross-layer energy efficiency optimization model for WBAN using IR-UWB transceivers

An energy efficiency optimization model for the IEEE 802.15.6 standard based wireless body area network is described in this paper. Cross-layer approach has been used here by focusing on physical (PHY) and medium access control (MAC) layers taking into account relevant characteristics therein. Studied PHY is based on impulse radio ultra wideband (IR-UWB) signaling with mandatory on---off keying modulation and non-coherent energy detection receiver. The analyzed MAC protocol is slotted Aloha which is used in the contention based mode of the IEEE 802.15.6 std. using IR-UWB PHY. The proposed model can be used to compare the energy efficiency of uncoded and coded transmissions using different Bose---Chaudhuri---Hocquenghem code rates. The model enables joint code rate and packet length optimization in AWGN channel when using the path loss model for hospital scenario, defined by the IEEE P802.15 working group. Results clearly show the most energy efficient code rate and packet length as a function of distance.

Hierarchical Architecture for Multi-Technology Wireless Sensor Networks for Critical Infrastructure Protection

A hierarchical architecture for wireless sensor network (WSN) consisting of heterogeneous devices is introduced in this paper. Proposed architecture is well suited for surveillance of critical infrastructures and it is designed to be scalable for various different scenarios. Low power consumption will be achieved by utilizing a wake-up radio concept which enables to keep the most power consuming devices at the sleep mode as long as possible. A WSN OpenAPI gateway (WOAG) component of the architecture supports high scalability by enabling data collection and sharing from networks deployed using multiple different technologies. WOAG facilitates WSNs information availability to local and remote end-users. Analytical energy efficiency optimization model for the architecture is developed. Results show energy efficiency gains that can be achieved with the proposed wake-up concept based intelligent hierarchical architecture design. For low event frequency case the energy efficiency is found to be one order of magnitude better than for duty cycle (1 %) radio based network.