A Household Electricity Consumption Algorithm with Upper Limit

Jin, Wen-Jie; Feng, Yi; Zhou, N.; Zhong, Xiao

doi:10.1109/wcsn.2014.93

Cited by 7 publications

(6 citation statements)

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“…In addition, it can also help reduce the energy consumption of buildings. The construction industry consumes nearly 40% of the world's energy [2], and about 50% of consumption is caused by air conditioning [3]. Adjusting the temperature of air conditioning based on individual thermal comfort can maintain low energy consumption and retain comfort simultaneously [4].…”

Section: Introductionmentioning

confidence: 99%

A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space

2019

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It is crucial to evaluate indoor personal thermal comfort for a comfortable and green thermal environment. At present, the research on individual thermal comfort does not consider its implementation mode. Moreover, the improvement of energy saving efficiency under the premise of increasing human comfort is an urgent problem that needs to be solved. In this paper, we proposed a Building Information Model (BIM) and Artificial Neural Network (ANN) based system to solve this problem. The system consists of two parts including an ANN predictive model considering the Predicted Mean Vote (PMV) index, the persons’ position, and an innovative plugin of BIM to realize dynamic evaluation and energy efficient design. The ANN model has three layers, considering three environment parameters (air temperature, air humidity, and wind speed around the person), three human state parameters (human metabolism rate, clothing thermal resistance, and the body position) and four body parameters (gender, age, height, and weight) as inputs. The plugin provides two functions. One is to provide corresponding personal thermal comfort evaluation results with dynamic changes of parameters returned by Wireless Sensor Networks (WSN). The other one is to provide energy saving optimization suggestions for interior space design by simulating the energy consumption index of different design schemes. In the data test, the Mean Squared Error (MSE) of the established ANN model was about 0.39, while the MSE of traditional PMV model was about 2.1. The system realized the integration of thermal information and a building model, thereby providing guidance for the creation of a comfortable and green indoor environment.

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Section: Introductionmentioning

confidence: 99%

A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space

2019

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“…. [1][2][3]5,7,[10][11][12][13]22,[27][28][29][30][31][32]34,35,[45][46][47]. The neural networks used in it introduce flexibility to the formulation of models describing comfort.…”

Section: Discussionmentioning

confidence: 99%

“…Thanks to this, one can talk about a kind of individual approach to the topic, while taking into account a set of features representing a given community. The described gap was noticed some time ago, thanks to which there is currently a flood of publications on this subject [1][2][3]5,7,[10][11][12]22,[27][28][29][30][31][32][33][34][35][45][46][47]. It is worth quoting the work in [22], in which it is stated that the establishment of an individual thermal comfort evaluation is essential to achieving personalized thermal environment management.…”

Section: The Present Research Gap Literature Reviewmentioning

confidence: 99%

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Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer

Dudzik

2020

Sustainability

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Modelling of comfort with the use of neural networks in modern times has become extremely popular. In recent years, scientists have been using these methods because of their satisfactory accuracy. The article proposes a method of modelling feedforward neural networks, thanks to which it is possible to obtain the most efficient network with one hidden layer in terms of a given quality criterion. The article also presents the methodology for modelling a PMV index, on the basis of which it can be demonstrated whether the network will work properly not only on paper but in reality as well. The objective of this work is to develop a performance model allowing the effective improvement of all electrical and mechanical devices affecting the energy efficiency and indoor environment in smart buildings. To achieve this, several attributes of indoor environment are included, namely: air leakage as a connection to the outdoor environment, but also as uncontrolled component of energy, ventilation as delivery and distribution of fresh air in the building space, individual ventilation on demand indoor air quality (IAQ) in the dwelling or as a personal IAQ control, source control of pollutants in the building, thermal comfort, temperature, air movement and humidity control (humidity modifiers, i.e., buffers different from the air conditioning radiation from cold and hot surfaces bringing forward a question about the strategy of the process control. One may either develop a series of control models to be synthesized later or one can use one over-arching characteristic and use its components for operating the control system. The paper addresses the second strategy and uses the concept of PMV for a criterion of broadly defined thermal comfort (including ventilation and air quality).

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“…Recent papers have presented techniques and various algorithms for monitoring electric power consumption in individual households [14,15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the influence of the current drawn by the appliance on the close magnetic field

et al. 2019

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<p class="Abstract">The development of technology and the corresponding increase in living standards imply the generalized use of the most diversified and sophisticated equipment, a distinct, significant category being household appliances. The frequent presence of these devices close to a person’s heart, eyes, or brain naturally gives rise to concerns about the level of the associated (low-frequency) magnetic fields. The main objective of our paper is to study the possible correspondence between the low-frequency magnetic field generated by some household appliances and their current consumption. The proposed measurement system consists of the hand-held triple axis 480826 EMF Tester manufactured by Extech Instruments and the 80i-400 Fluke clamp meter. The performed measurement methodology takes into account the operating principles of the appliance. We perform measurements for a large selection of devices (47). The measuring point for the magnetic field is set at 30 cm from the source. Finally, by applying the Biot-Savart law to the previously determined drawn current, we calculate the magnetic field. We also compare the corresponding, directly measured values and analyze them.</p>

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A Household Electricity Consumption Algorithm with Upper Limit

Cited by 7 publications

References 1 publication

A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space

A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space

Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer

Analysis of the influence of the current drawn by the appliance on the close magnetic field

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