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During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) play a major role in the wide variety of applications like underground pipeline and leaks monitoring, temperature distribution monitoring in industrial cyber systems, military, forest life monitoring, and environmental and geographical monitoring. Sensors are widely used in these different applications. The number of sensors and the application concerned mainly decides the energy consumption, network lifetime. In this process relay nodes may help the sensors as backbone to connect with sink node or base stations. In this paper, we introduce a new approach for relay node selection in WSN to minimize the energy consumption of the network. It uses channel aware relay selection technique using game theory optimization and act as a virtual backbone in connecting to the base station. However, the relay nodes are varied to check the optimal number of relays required for the small, medium and large number of nodes deployed in the network. Simulations are carried out using Network Simulator NS-2.35 and network is analyzed in wide variety of scenarios. Results show that the proposed relay node selection algorithm reduces energy consumption, improves lifetime, throughput of the network. [ABSTRACT FROM AUTHOR]

Today, continuous technical and emerging advances between power communication systems and smart grids and applying swarm intelligence have increased for data sharing and analytics in our life. On the other side, Internet of things (IoT) has important key role to establish constructive interactions between smart devices and smart grid and power communication applications. For enhancing data transformation and improvements of multi-objective Quality of Service (QoS) factors, Swarm Optimization Techniques (SOT) are applied simultaneously in a cooperative smart environment to solve NP-hard problems. This paper provides a comprehensive analysis to address a new technical taxonomy and categorization of existing SOT-based smart grid applications in power communication systems in the IoT. Also, existing service and resource management case studies on smart grids and power communication systems are briefly analyzed and discussed. Existing evaluation factors on smart grid applications using SOT are represented. Possible advantages and weaknesses of each category are discussed with respect to new challenges and open research directions. [ABSTRACT FROM AUTHOR]

The most installed Renewable Energy Sources (RES) in micro-grids (MG) are Photovoltaic (PV) power and wind power. Due to the intermittent behaviour of renewable sources, parallel operation of RES and battery storage known as hybrid system is important particularly in remote micro-grids to reduce the fuel consumption by diesel generators and continuity of supply to the load. In this paper, multilevel inverter called Packed E-Cell (PEC) is used for parallel operation of RES and battery storage optimally for micro-grid applications. The PEC requires less components compared to other Multi-level inverters (MLI) topology with relatively low total harmonic distortion (THD). Further, selective harmonic technique based on optimization principle is used to enhance the harmonic profile using low frequency switching technique. The 3rd and 5th harmonics are eliminated using Genetic Algorithm (GA) optimization technique. The simulation-based analysis is done using Simulink/MATLAB and the results obtained for THD in the output current and voltage are presented and discussed in the paper. A comparative analysis is also presented with high frequency modulation technique phase disposition pulse width modulation (PDPWM) technique. The experimental validation of the proposed scheme is done using Typhoon HIL (hardware in loop). [ABSTRACT FROM AUTHOR]

Recently, energy-saving scheduling issues have attracted more and more attention in the manufacturing field. Meanwhile, in practical production, maintenance planning is viewed as a vital task in the workshop. However, the existing literature about energy-saving scheduling problems rarely consider the effect of preventive maintenance. Therefore, this paper investigates an energy-saving flexible job shop scheduling problem with preventive maintenance. A mathematical model is proposed considering the minimization of total energy consumption. To solve the problem, a novel discrete elephant herding optimization algorithm (NDEHO) is proposed according to the problem's characteristics. To test the NDEHO's performance, the Taguchi design of experiment approach is adopted to get the best combination of parameters in the algorithm. Numerical experiments are conducted based on twenty-four instances, including four benchmark instances and twenty randomly generated instances. Computational data indicate that NDEHO outperforms other compared algorithms for solving the considered problem. [ABSTRACT FROM AUTHOR]

Due to the fact that the real manufacturing processes are often constrained by many kinds of resources and the trend that the energy consumption of factories is regulated more and more strictly, this paper studies the energy-efficient multi-resource flexible job shop scheduling problem (EE-MRFJSP). The goal is to minimize the energy consumption and completion time for all of the jobs' production. Firstly, a general mathematic model for EE-MRFJSP is set up, in which the unit energy consumptions of the main resource's different states are varied, and a constraint formula to ensure no crossover working periods for any resource is included. Then, a non-dominated sorting teaching-learning-based optimization(NSTLBO) algorithm is proposed to solving the problem, the details of NSTLBO include the real encoding method, Giffler Thompson rule for decoding, non-dominated sorting rule to rank the pareto sets and crowding distance of solution for maintaining the population's diversity, and the traditional two evolving stages: teacher education and student mutual study. Finally, comparative experiments are made based on some new designed instances, and the results verify our proposed NSTLBO algorithm can effectively solve the EE-MMFJSP, and has obvious advantages by comparing with NSGA-II, NRGA, and MOPSO. [ABSTRACT FROM AUTHOR]

Due to the mobility and frequent topology changes, routing the data poses a challenging issue in the Internet of Things (IoT). Security poses a fundamental requirement factor in most IoT applications particularly, secure routing among the IoT nodes. The aim of this paper is to develop secure routing in IoT with minimal delay, minimal energy consumption and high throughput. Thus, a new algorithm, named Rider Foraging Optimization algorithm (RFO) is designed by the incorporation of standard Rider Optimization algorithm (ROA) and Bacterial Foraging optimization algorithm (BFO) that render an optimal solution, which is the optimal path for transmitting the information. Initially, the trust of the nodes is evaluated and the cluster head (CH) is chosen based on Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol to securely transmit the data. Then, the proposed RFO algorithm selects the optimal path using the fitness factors, such as trust, energy, and delay constraints. The results revealed that the energy consumption of the nodes is reduced and the lifetime of the network is enhanced through the implementation of the proposed RFO, which attained a maximal throughput of 1, and minimal delay and energy consumption of 0.1169 and 0.0002, respectively. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced version of the emperor penguin optimization algorithm is proposed for solving dynamic economic dispatch (DED) problem incorporating renewable energy sources and microgrid. Dynamic economic load dispatch optimally shares the power on an hourly basis for a day among the committed generating units to satisfy the feasible load demand. Emission of pollutants from the combustion fossil fuel and gradual depletion of fossil fuel encourages the usage of renewable energy sources. Implementation of renewable energy sources with the reinforcement of green energy transforms the fossil fuel-based plant into a hybrid generating plant. The increase in power production with the increase in electricity demand implicates challenges for economical operation. The proposed algorithm is applied to the DED problem for fossil fuel based and renewable energy system to find economic schedule of generated power among the committed generating units. The proposed optimization algorithm is inspired by the huddling behavior of the emperor penguin. The exploration strategy is enhanced by adapting oppositional based learning. Chaotic mapping is used to maintain a proper balance between exploration and exploitation in the entire search space, which minimizes the cost of generation in the power system. [ABSTRACT FROM AUTHOR]

The nature of mobile nodes are affects the performance of MANET. The main requirement of secured MANET is use of secure protocols which ensure the network integrity, confidentiality, availability and authenticity. Recently, the secure protocols have been used to resists such attacks in MANET. Spoofing attack is one of them, which affects network by an attacker program successfully acts on another node behalf by impersonating data. In this paper, we investigate the spoofing attack problems in MANET and propose an optimal intrusion detection system (OIDS) using hybrid optimization algorithm (HOA) named as OIDS-HOA. Here, we propose an improved whale optimization (IWO) algorithm for efficient cluster formation and a cluster chain weight metrics approach (CCWW) for trust degree computation of each mobile node. Moreover, the intrusion detection system (IDS) is done by the condition check algorithm and the intrusion response action (IRA) is performed by average route trust with three different responses are: No Punishment (NP), Route Change (RC) and Route Isolate (RI). The simulation results show that the effectiveness of proposed OIDS-HOA scheme in terms of packet delivery fraction, end-end-delay, energy consumption, overhead and packet drop ratio. [ABSTRACT FROM AUTHOR]

The cognitive radio network provides a pioneered solution to the spectrum scarcity problem and represents a new paradigm for designing intelligent wireless networks. Energy efficient cognitive radio system maintaining reliability holds great importance in the present scenario of wireless communications. In a cognitive radio network, relays are used to enhance energy efficiency as well as to maintain the sensing reliability. Most of the works in the area of cognitive radio network focused on optimization of energy consumed during data transmission only, while neglecting the energy consumed during spectrum sensing. In this paper, an energy efficient multi-relay cognitive radio network is designed, in which both sensing energy and data transmission energy are jointly optimized. Also, optimal values of system parameters like sensing time and amplifying gain of the relays are determined for the energy efficient system. The minimization of the energy consumed under constraints of target throughput and sensing requirements of cognitive radio network is considered as an optimization problem. Swarm intelligence based optimization techniques like particle swarm optimization (PSO), Particle Swarm Optimization with Aging Leader and Challengers (ALCPSO), Human behavior based Particle Swarm Optimization (HPSO) and Whale Optimization Algorithm (WOA) are used to optimize energy consumption in the network. The analysis reveals that the proposed scheme makes the cognitive radio network more energy efficient than conventional schemes. [ABSTRACT FROM AUTHOR]

Conversion of the conventional vehicle (CV) into the plug-in hybrid electric vehicle (PHEV) is one of the promising solutions to improve transport sustainability and reduce outdoor air pollution. Energy management is crucial for the performance of PHEV. The paper presents combine rule based-artificial bee colony optimization algorithm for energy management of converted plug-in hybrid electric vehicle (CPHEV). The diesel operated parallel hybrid topology is considered for study with the designed electric powertrain. NOx and PM are considered as optimization parameters along with specific fuel consumption. The performance based on fuel consumption and emissions (NOx and PM) is analyzed by considering sample Indian urban and highway driving cycle. The complete vehicle is simulated using MATLAB Simulink linked with coding. The results of converted PHEV obtained is compared with conventional one for both driving cycles for analysis of the fuel consumption and emissions considering real-time benchmarking norms. The results indicate that the combine rule based-artificial bee colony strategy keeps pollution under control required as per BSIII norms. [ABSTRACT FROM AUTHOR]

Named Data Network (NDN) has various routing and forwarding strategies, but most of which have failed to achieve the ability of Quality-of-Service (QoS), such as high resource consumption and low transmission efficiency. Leveraged by antcolony optimization (ACO), a QoS-supported routing forwarding strategy is proposed in this paper to address those problems. Specifically, the proposed strategy is able to consistently select a route which meets the QoS requirement and optimal routing strategy is realized in NDN network. Experimental results show that the proposed routing strategy outperforms the random forwarding strategy in terms of average delay, costs, and packet loss rate in NDN. [ABSTRACT FROM AUTHOR]