Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

Lee, Seung-hui; Jung, Sungwon; Lee, Jae‐Wook

doi:10.3390/en12040608

Cited by 51 publications

(43 citation statements)

References 33 publications

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“…Artificial neural networks are alternative calculation techniques that are used in many areas [37,38] for the prediction [39,40] and the classification of large data sets and their analysis (e.g., in the context of finding cause and effect relationships between data) [41], data matching (especially in the event of information overload), and optimization [42,43].…”

Section: Methodsmentioning

confidence: 99%

Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process

Tutak

Brodny

2019

Energies

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With regard to underground mining, methane is a gas that, on the one hand, poses a threat to the exploitation process and, on the other hand, creates an opportunity for economic development. As a result of coal exploitation, large amounts of coal enter the natural environment mainly through ventilation systems. Since methane is a greenhouse gas, its emission has a significant impact on global warming. Nevertheless, methane is also a high-energy gas that can be utilized as a very valuable energy resource. These different properties of methane prompted an analysis of both the current and the future states of methane emissions from coal seams, taking into account the possibilities of its use. For this reason, the following article presents the results of the study of methane emissions from Polish hard coal mines between 1993–2018 and their forecast until 2025. In order to predict methane emissions, research methodology was developed based on artificial neural networks and selected statistical methods. The multi-layer perceptron (MLP) network was used to make a prognostic model. The aim of the study was to develop a method to predict methane emissions and determine trends in terms of the amount of methane that may enter the natural environment in the coming years and the amount that can be used as a result of the methane drainage process. The methodology developed with the use of neural networks, the conducted research, and the findings constitute a new approach in the scope of both analysis and prediction of methane emissions from hard coal mines. The results obtained confirm that this methodology works well in mining practice and can also be successfully used in other industries to forecast greenhouse gas and other substance emissions.

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Section: Methodsmentioning

confidence: 99%

Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process

Tutak

Brodny

2019

Energies

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“…The weight of each calculated element is used as the input of the activation function. The output is derived through the sum of the weighted values [15]. The activation function utilized the most commonly used sigmoid function: When an ANN prediction model is created, hidden nodes and layers must be constructed, as shown in Figure 5.…”

Section: Prediction Of Energy Consumption Using Annsmentioning

confidence: 99%

“…Research on a prediction model that integrates user information with the model based on such physical attributes is required. Lee et al [15] derived the energy consumption from the same buildings according to the behavioral patterns of the users, and implemented a DNN model for predicting energy consumption through six elements, i.e., gender, age, occupation, income, education level, and length of residency. Their prediction model exhibited 64% accuracy, indicating that the six elements had an impact on energy consumption.…”

Section: Prediction Of Energy Consumption Using Annsmentioning

confidence: 99%

“…Single-person households and families without children consumed less energy, because they were socially and economically more active and thus spent less time in their houses. However, energy consumption increased upon childbirth, when their residence time and economic activity patterns changed [15]. Figure 1 shows the research process of this study.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

2019

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When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influential in all seasons: the number of exterior walls, housing direction, housing area, number of years occupied, number of household members, and the occupation of the household head. The elements that had an impact in each period were then derived. Based on the results of the regression analysis, input variables for the training of an artificial neural network (ANN) model were selected for each period, and residential energy consumption prediction models were implemented based on actual consumption. The elements identified as those affecting energy consumption, through regression analysis, can be used for implementing prediction models with advanced forms. This study is significant in that we derived influential elements from an integrative perspective.Sustainability 2020, 12, 109 2 of 28 Thus far, research on how the physical environment affects energy consumption has been intensively conducted; however, it is difficult to collect a large amount of user information owing to its personal nature. It is also difficult to quantitatively reduce and analyze the information on individuals. Nevertheless, the analysis of qualitative data has become possible through national surveys and new methodologies that are capable of nonlinear analysis, such as artificial neural networks (ANNs). In this study, the energy consumption in residential buildings in South Korea was investigated, by integrating the physical characteristics of residential buildings with information on occupant behavior and characteristics, to identify elements that affect residential energy consumption. The elements that were found to be influential were subsequently used to construct energy consumption prediction models. Literature ReviewMost studies on the energy consumption of residential buildings have focused on physical characteristics, and those that considered user behavior only considered one or two user elements. However, user information cannot be defined with such few elements; therefore, studies that reflect user information from various perspectives are required. In this study, elements that affect energy consumption were derived considering both the physical characteristics of buildings and user information. Effects of Physical Building Characteristics on Energy ConsumptionKim [4] reported that annual power consumption varied depending on the housing type, construction year, number of floors, building structure, and building location. It was found that ...

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“…The use of information about occupant behavior to control the HVAC system and estimate possible energy savings depends on the thermal behavior of the building, which determines the heating and cooling time of the building. Several studies have been carried out to investigate the building thermal behavior and model predictive control (MPC), which allow better tracking of changes in the operating mode and temperature set points [34]. The knowledge of building thermal behavior and the popular gray box model approach are the basis for designing an HVAC control system and estimating the energy savings potential [35,36].…”

Section: Introductionmentioning

confidence: 99%

Impact of Heating Control Strategy and Occupant Behavior on the Energy Consumption in a Building with Natural Ventilation in Poland

Kaminska

2019

Energies

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This study aims to provide an experimental assessment of energy consumption in an existing public building in Poland, in order to analyze the impact of occupant behavior on that consumption. The building is naturally ventilated and the occupants have the freedom to change the temperature set point and open or close the windows. The energy consumption is calculated and the calculation results are compared with the experimental data. An analysis of occupants’ behavior has revealed that they choose temperature set points in a wide range recognized as thermal comfort, and window opening is accidental and difficult to predict. The implemented heating control algorithms take into account the strong influence of individual occupant preferences on the feeling of comfort. The energy consumption assessment has revealed that the lowering of temperature set point by 1 °C results in an energy saving of about 5%. Comparisons of energy consumption with heating control and without any controls showed that the potential for energy reduction due to heating control reached approximately 10%. The use of windows control, which allows to turn off the heating after opening the window and its impact on energy savings have been discussed as well.

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Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

Cited by 51 publications

References 33 publications

Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process

Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process

Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

Impact of Heating Control Strategy and Occupant Behavior on the Energy Consumption in a Building with Natural Ventilation in Poland

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