Ming Liu¹, Pinghua Zhu^2,*, Xiancui Yan², Haichao Li², Xintong Chen²

Journal of Renewable Materials, Vol.12, No.2, pp. 329-347, 2024, DOI:10.32604/jrm.2023.045381

Abstract As socioeconomic development continues, the issue of building energy consumption has attracted significant attention, and improving the thermal insulation performance of buildings has become a crucial strategic measure. Simultaneously, the application of solid waste in insulation materials has also become a hot topic. This paper reviews the sources and classifications of solid waste, focusing on research progress in its application as insulation materials in the domains of daily life, agriculture, and industry. The research shows that incorporating household solid waste materials, such as waste glass, paper, and clothing scraps into cementitious thermal insulation can significantly reduce the thermal conductivity of… More >

Noman Khan^1,2, Samee Ullah Khan^1,2, Sung Wook Baik^1,2,*

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 >

Jian Ai¹, Jie Xue^1,*, Jiabang Yu^1,2, Xinyu Huang², Pan Wei^1,2, Xiaohu Yang², Bengt Sundén^3,*

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 >

Na Dong^1,*, Xiao Yang², Nasser Yousefi^3,4,*

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 >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy², Amel Ali Alhussan^1,*, 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 >

Wan Norsyafizan W. Muhamad¹, Kaharudin Dimyati², Muhammad Awais Javed³, Suzi Seroja Sarnin^1,*, 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 >

Hatem A. Alharbi¹, Barzan A. Yosuf², Mohammad Aldossary^3,*, 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 >

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 >

Ali Chitsazan^a , Georg Klepp^a, Mohammad Esmaeil Chitsazan^b, Birgit Glasmacher^c

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 >

You Lu^1,2,#,*, Linqian Cui^1,2,#,*, Yunzhe Wang^1,2, Jiacheng Sun^1,2, 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 >