Fuzzy logic-based advanced on–off control for thermal comfort in residential buildings

Kang, Chang-Soon; Hyun, Chang‐Ho; Park, Mignon

doi:10.1016/j.apenergy.2015.05.119

Cited by 44 publications

(8 citation statements)

References 14 publications

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“…In another work, Zang et al integrated a genetic algorithm (GA), an artificial neural network (ANN), multivariate regression analysis (MRA), and a fuzzy logic controller (FLC) to optimize the indoor environment and energy consumption based on simulation results [11]. In a similar work, Kang et al [12] developed a fuzzy-logic based on-off control system for thermal comfort with online learning capability. Recently, companies such as Nest Labs [13] and Honeywell [14] have developed intelligent thermostats which enable autonomous control of the HVAC.…”

Section: Intelligent Hvac Controlmentioning

confidence: 99%

Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses

et al. 2020

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Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reduction by pre-trained model over baseline when validated in a simulation environment and achieved up to 21% cost reduction when deployed in the real house. This finding provides experimental evidence that the pre-trained DRL has the potential to adapt to different house environments and comfort settings.

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Section: Intelligent Hvac Controlmentioning

confidence: 99%

Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses

et al. 2020

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“…The FLC is a computational intelligence techniques converting the human experience into an advanced control method. Unlike the binary system, the FLC is capable of addressing issues in uncertain and heuristic ways [38][39][40][41]. In the single household PV storage system, the battery power and grid power are regarded as controllable variables i.e., power source or power sink to satisfy power balance and the load demand as in Equation (3).…”

Section: Design Of the Fuzzy Controllermentioning

confidence: 99%

A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community

et al. 2018

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Self-consumption of household photovoltaic (PV) storage systems has become profitable for residential owners under the trends of limited feed-in power and decreasing PV feed-in tariffs. For individual PV-storage systems, the challenge mainly lies in managing surplus generation of battery and grid power flow, ideally without relying on error-prone forecasts for both generation and consumption. Considering the large variation in power profiles of different houses in a neighborhood, the strategy is also supposed to be beneficial and applicable for the entire community. In this study, an adaptable battery charging control strategy is designed in order to obtain minimum costs for houses without any meteorological or load forecasts. Based on fuzzy logic control (FLC), battery state-of-charge (SOC) and the variation of SOC (∆SOC) are taken as input variables to dynamically determine output charging power with minimum costs. The proposed FLC-based algorithm benefits from the charging battery as much as possible during the daytime, and meanwhile properly preserves the capacity at midday when there is high possibility of curtailment loss. In addition, due to distinct power profiles in each individual house, input membership functions of FLC are improved by particle swarm optimization (PSO) to achieve better overall performance. A neighborhood with 74 houses in Germany is set up as a scenario for comparison to prior studies. Without forecasts of generation and consumption power, the proposed method leads to minimum costs in 98.6% of houses in the community, and attains the lowest average expenses for a single house each year.

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“…Kedua adalah amplitudo osilasi respon yang memengaruhi akurasi pengendalian dan besar rugi-rugi energi pada respon sistem kendali keseluruhan. Beberapa strategi digunakan untuk memperbaiki performansi pengontrol on/off, seperti memadukan pengontrol on/off dengan pengontrol fuzzy [10][11][12], dan pengontrol model predictive [13]. Oleh karena itu, diperlukan suatu strategi untuk mengatur parameter frekuensi dan amplitudo osilasi respon.…”

Section: Pendahuluanunclassified

Simulasi Pengontrol On/Off pada Sistem Kendali Umpan Balik dengan Model Fisis Elektronik

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et al. 2018

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Pengontrol on/off merupakan salah satu jenis aksi pengontrolan yang banyak digunakan pada pengontrol otomatis karena sederhana dan relatif murah. Terdapat dua parameter respon sistem kendali yang harus dipertimbangkan pada aplikasi pengontrol on/off. Pertama adalah frekuensi osilasi respon yang menentukan osilasi aktuator dan memengaruhi umur ketahanan komponen aktuator. Kedua adalah amplitudo (deviasi) osilasi respon yang menentukan akurasi dan efisiensi energi pengontrolan. Makalah ini membahas kajian pengaruh parameter waktu tunda histeresis, T dan penguatan, M pengontrol on/off tersebut terhadap respon kendali umpan balik. Kajian dilakukan melalui simulasi MATLAB. Hasil simulasi dibandingkan dengan implementasi menjadi sistem fisis yang dibangun dengan suatu mikrokontroler digital, aktuator saklar elektronik, dan rangkaian elektronik proses dan komponen umpan balik dari komponen pasif resistor dan kapasitor (RC). Hasil simulasi menunjukkan bahwa semakin besar nilai T maka waktu yang dibutuhkan oleh sistem untuk mencapai keadaan mantap semakin lama, frekuensi osilasi keluaran semakin kecil, amplitudo osilasi respon semakin besar pula. Semakin besar nilai parameter M, maka waktu yang dibutuhkan oleh sistem untuk mencapai keadaan mantap semakin cepat, amplitudo respon semakin besar dan frekuensi osilasi semakin kecil. Berdasarkan pengujian, dapat disimpulkan bahwa respon pengontrolan umpan balik dengan menggunakan pengontrol on/off dapat diatur sesuai kriteria pengontrolan dengan mengatur parameter delay histeresis dan penguatan pengontrol on/off.

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Fuzzy logic-based advanced on–off control for thermal comfort in residential buildings

Cited by 44 publications

References 14 publications

Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses

Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses

A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community

Simulasi Pengontrol On/Off pada Sistem Kendali Umpan Balik dengan Model Fisis Elektronik

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