An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management

Lork, Clement; Li, Wen‐Tai; Yan, Qing; Zhou, Yuren; Yuen, Chau; Tushar, Wayes; Saha, Tapan Kumar

doi:10.1016/j.apenergy.2020.115426

Cited by 77 publications

(35 citation statements)

References 30 publications

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“…4 shows a general comparison of the DDSL controller approach with three QL state-ofthe-art methods. The stated studies [43][44][45] used data-driven QL approaches to solve their control requirement in addition to the DDSL controller. The major difference between the individual approaches is the way the QL algorithm is used, the possible additional methods for control, and the monitored subject matter.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Prauzek¹,

Paterova²,

Konecny³

et al. 2022

Computers, Materials &Amp; Continua

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Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices. The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values, leading to effective operation of power-aware systems. The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm. The root mean square error (RMSE) and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria (the performance score parameter and normalized geometric distance) were used for overall evaluation and comparison. The experiments showed that the novel DDSL method reaches significantly lower RMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm. The overall criteria performance score is 40% higher than the reference algorithm base on static confirmation settings.

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Section: Discussionmentioning

confidence: 99%

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Prauzek¹,

Paterova²,

Konecny³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…However, network resources may be over-consumed during the training and data transmission process. To solve the complex and dynamic control issues, a federated deep reinforcement learning-based [101,107,108,118,123,133,134,136,144,145,154,159,161,165,167,168,171], [174-177, 179, 183, 188, 192, 198, 200], [204,208,210,213,216,221,222,[225][226][227] [177-179, 181, 187, 188, 190, 196, 197, 200, 201, 203, 206, 207, 211, 214, 218, 223], [234,236,239,243,245,248,252,254,258,275,[277][278][279], [284, 286-292, 294, 297, 300], [305,306, Wireless, radio, antenna, signal [14,18,28…”

Section: Centralized and Federated Methodsmentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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“…They starts to service some services, including secondary frequency regulation [4], [5], peak load shaving [6], [7] and capacity reserve [8], [9], to facilitate the grid reliability. The residential air-conditioners' (AC) electricity consumption accounts for 30% to 50% of the total urban [10], [11]. The large capacity and number of owners endue the AC resources' great demand response (DR) potential and significant investment chances.…”

Section: Parameters θSetmentioning

confidence: 99%

Optimal Allocation Method of Residential Air-Conditioners: Trade-Off Solutions Between Economic Costs and Aggregation Reliability

Han

Guo

et al. 2022

IEEE Open J. Power Energy

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An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management

Cited by 77 publications

References 30 publications

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

Optimal Allocation Method of Residential Air-Conditioners: Trade-Off Solutions Between Economic Costs and Aggregation Reliability

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