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  • Open Access


    Deep Autoencoder-Based Hybrid Network for Building Energy Consumption Forecasting

    Noman Khan1,2, Samee Ullah Khan1,2, Sung Wook Baik1,2,*

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 153-173, 2024, DOI:10.32604/csse.2023.039407

    Abstract Energy management systems for residential and commercial buildings must use an appropriate and efficient model to predict energy consumption accurately. To deal with the challenges in power management, the short-term Power Consumption (PC) prediction for household appliances plays a vital role in improving domestic and commercial energy efficiency. Big data applications and analytics have shown that data-driven load forecasting approaches can forecast PC in commercial and residential sectors and recognize patterns of electric usage in complex conditions. However, traditional Machine Learning (ML) algorithms and their features engineering procedure emphasize the practice of inefficient and ineffective techniques resulting in poor generalization.… More >

  • Open Access


    Heat and Humidity Transport Analysis Inside a Special Underground Building

    Jian Ai1, Jie Xue1,*, Jiabang Yu1,2, Xinyu Huang2, Pan Wei1,2, Xiaohu Yang2, Bengt Sundén3,*

    Frontiers in Heat and Mass Transfer, Vol.21, pp. 47-63, 2023, DOI:10.32604/fhmt.2023.045134

    Abstract The calculation of heat and humidity load serves as the cornerstone of Heating, Ventilation, and Air Conditioning (HVAC) design. Nevertheless, as the heat and humidity load characteristics of underground structures differ substantially from those of above-ground structures, it is a challenge to derive their accurate calculation procedure through engineering experience. Therefore, it is particularly important to carry out quantitative research on heat and humidity load. This study used Design Builder software to study the influence of the design state point of air conditioning in underground buildings on energy consumption. The study showed that compared with the single design temperature of… More >

  • Open Access


    Optimization of Chiller Loading Problem Using Improved Golden Jackal Optimization Algorithm Leads to Reduction in Energy Consumption

    Na Dong1,*, Xiao Yang2, Nasser Yousefi3,4,*

    Energy Engineering, Vol.120, No.11, pp. 2565-2583, 2023, DOI:10.32604/ee.2023.029862

    Abstract This paper proposes a modified golden jackal optimization (IGJO) algorithm to solve the OCL (which stands for optimal cooling load) problem to minimize energy consumption. In this algorithm, many tools have been developed, such as numerical visualization, local field method, competitive selection method, and iterative strategy. The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed. In order to fully utilize the effectiveness of the proposed algorithm, three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works. The results show… More >

  • Open Access


    Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2, Amel Ali Alhussan1,*, Marwa M. Eid3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811

    Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic… More >

  • Open Access


    Survey of Resources Allocation Techniques with a Quality of Service (QoS) Aware in a Fog Computing Environment

    Wan Norsyafizan W. Muhamad1, Kaharudin Dimyati2, Muhammad Awais Javed3, Suzi Seroja Sarnin1,*, Divine Senanu Ametefe1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1291-1308, 2023, DOI:10.32604/cmc.2023.037214

    Abstract The tremendous advancement in distributed computing and Internet of Things (IoT) applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing. Thus, suitable and effective applications could be performed to satisfy the applications’ latency requirement. Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution. Effective resource management techniques can improve the quality of service metrics. Due to the limited and heterogeneous resources available within the fog infrastructure, the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT network.… More >

  • Open Access


    Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

    Hatem A. Alharbi1, Barzan A. Yosuf2, Mohammad Aldossary3,*, Jaber Almutairi4

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1299-1319, 2023, DOI:10.32604/csse.2023.039367

    Abstract In this paper, the Internet of Medical Things (IoMT) is identified as a promising solution, which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service (QoS) in the healthcare sector. However, problems with the present architectural models such as those related to energy consumption, service latency, execution cost, and resource usage, remain a major concern for adopting IoMT applications. To address these problems, this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming (MILP), with the objective of efficiently processing and placing IoMT… More >

  • Open Access


    Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

    S. Balaji*, S. Karthik

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 727-743, 2023, DOI:10.32604/iasc.2023.037409

    Abstract The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption; this is because EC is intimately tied to other forms of energy, such as oil and natural gas. For the purpose of determining and bettering overall energy consumption, there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things (IoT). Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable, and it has proven to be an effective tool for… More >

  • Open Access



    Ali Chitsazana , Georg Kleppa, Mohammad Esmaeil Chitsazanb, Birgit Glasmacherc

    Frontiers in Heat and Mass Transfer, Vol.18, pp. 1-6, 2022, DOI:10.5098/hmt.18.17

    Abstract For the optimization of the impinging round jet, the pressure force coefficient and drying energy consumption on the moving curved surface are set as the objective functions to be minimized simultaneously. SHERPA search algorithm is used to search for the optimal point from multiple objective tradeoff study (Pareto Front) method. It is found that the pressure force coefficient on the impingement surface is highly dependent on the jet to surface distance and jet angle, while the drying energy consumption is highly dependent on the jet to jet spacing. Generally, the best design study during the multi-objective optimization is found at… More >

  • Open Access


    Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

    You Lu1,2,#,*, Linqian Cui1,2,#,*, Yunzhe Wang1,2, Jiacheng Sun1,2, Lanhui Liu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 717-732, 2023, DOI:10.32604/cmes.2023.027032

    Abstract Most studies have conducted experiments on predicting energy consumption by integrating data for model training. However, the process of centralizing data can cause problems of data leakage. Meanwhile, many laws and regulations on data security and privacy have been enacted, making it difficult to centralize data, which can lead to a data silo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework. However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg) method is used to directly weight the model parameters on average, which may have an adverse effect on… More >

  • Open Access


    A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

    Zerui Zhen1, Zihao Wu2, Lei Feng1,*, Wenjing Li1, Feng Qi1, Shixuan Guo1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2939-2955, 2023, DOI:10.32604/cmc.2023.036505

    Abstract Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the resource consumption caused by frequent… More >

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