Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model

Fan, Chengliang; Ding, Yong

doi:10.1016/j.enbuild.2019.05.043

Cited by 94 publications

(25 citation statements)

References 45 publications

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“…In future works, we will consider the application of this work for IoT networks, such as urban environment improvement [34][35][36] and environmental monitoring [37][38][39][40]. Moreover, we will incorporate the intelligent algorithms such as learning-based algorithms [41,42], deep learning [43,44], and reinforcement learning [45][46][47] into the considered system, in order to further enhance the network performance.…”

Section: Discussionmentioning

confidence: 99%

Cache-aided mobile edge computing for B5G wireless communication networks

Xia

Lai

et al. 2020

J Wireless Com Network

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This paper investigates a cache-aided mobile edge computing (MEC) network, where the source offloads the computation task to multiple destinations with computation capacity, with the help of a cache-aided relay. For the proposed cache-aided MEC networks, two destination selection criteria have been proposed to maximize the computation capacity of the selected destination, the channel gain of relay link and the channel gain of direct link, respectively. Similarly, three destination selection criteria have been proposed for the cache-free MEC networks based on the computation capacities of destinations and the channel gains of transmission links, respectively. To evaluate the system performance regarding the latency constraint, we provide the outage probability for the proposed network which is defined based on the transmission-plus-computation time. Our analysis suggests that caching can significantly alleviate the impact of increasing the size of computation task, since only half of the transmission time of cache-free network is required. However, the cache-aided network can not fully exploit the signal from both direct and relay links, thus the improvement by caching is less significant in the high signal-to-noise ratio (SNR) region, compared with the cache-free network employing the destination with maximal channel gain of direct link. Numerical results are given to validate our analysis.

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Section: Discussionmentioning

confidence: 99%

Cache-aided mobile edge computing for B5G wireless communication networks

Xia

Lai

et al. 2020

J Wireless Com Network

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“…In future works, we will introduce deep learning-based [38][39][40] or Q-learning-based algorithms [41,42] to further improve the system performance. Moreover, we will apply the considered wireless techniques into some practical IoT systems [43,44] By substituting (A.3) into (A.7) and applying the binomial theorem, we have…”

Section: Discussionmentioning

confidence: 99%

Impact of Hardware Impairments with Imperfect Channel Estimation for Cache-Enabled UAV Relaying Networks

Deng

Rao

Zhu³

2020

Wireless Communications and Mobile Computing

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The effects of hardware impairments with imperfect channel estimation for cache-enabled UAV networks are studied in this paper. The effects of the setup parameters, such as the number of cached or relaying nodes, the hardware impairment factor, the channel estimation error, and the transmission SNR, on the outage probability are present by deriving the exact closed-form expressions on outage probability. Also, the asymptotic results are present when the transmission SNR is large enough. From the asymptotic analysis, we can see that there is an error floor introduced by the hardware impairments and imperfect channel estimation. Specifically, the error floor is dependent on the channel estimation error factor, the hardware impairment distortion factor, and the QoS requirements. And the diversity order of the error floor for the cached/relaying links is equal to the number of cached/relaying UAV nodes.

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“…According to the results obtained by the author; ANN is better suited to estimating the energy consumption of residential buildings because other models perform better compared to traditional statistical methods, namely linear regression analysis. Another study [65] used a multiple nonlinear regression model for cooling systems in public buildings to accurately estimate short-term cooling loads.…”

Section: Energy Demand and Neural Networkmentioning

confidence: 99%

While Covid-19 Outbreak Affects Economies and Societies; Exploring the Energy Demand in Turkey

Çoban¹

2021

European Journal of Technic

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Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model

Cited by 94 publications

References 45 publications

Cache-aided mobile edge computing for B5G wireless communication networks

Cache-aided mobile edge computing for B5G wireless communication networks

Impact of Hardware Impairments with Imperfect Channel Estimation for Cache-Enabled UAV Relaying Networks

While Covid-19 Outbreak Affects Economies and Societies; Exploring the Energy Demand in Turkey

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