Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building

Han, Xiao; Zhang, Chaohai; Tang, Yi; Ye, Yujian

doi:10.35833/mpce.2021.000050

Cited by 13 publications

(5 citation statements)

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“…The normalization summation method is used to solve the eigenvector W and the maximum eigenvalue λ max . Firstly, it normalizes the column vectors of the judgment matrix, and the calculation expression is shown in Equation (1).…”

Section: Construction Of Comprehensive Benefit Evaluation Index Syste...mentioning

confidence: 99%

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The Application of Geographic Information System in Urban Forest Ecological Compensation and Sustainable Development Evaluation

An,

Liu,

Hou

2024

Forests

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Urban forests can alleviate the urban heat island effect, improve air quality, and improve residents’ mental health. By studying urban forests, these resources can be better used and managed to create more livable urban environments. Therefore, the urban forest in the Taishan region is taken as the research object, and the ecological compensation and sustainable development of urban forest in Tai’an City are deeply analyzed by GIS. It divided the area into forest land, water bodies, wetlands, grasslands, and shrubs as the basic ecosystem types. And through secondary interpretation and combination, a complete urban forest information database was established. To evaluate the comprehensive benefits of urban forests, the analytic hierarchy process was utilized to establish a corresponding evaluation index system. Based on the assessment outcomes of the comprehensive benefits of urban forests in the area, a standard accounting method for urban forest ecological compensation was proposed. The results showed that each index of the comprehensive benefits of urban forests and the random consistency ratio were both less than 0.1. This indicated that the matrix calculation results of various indicators of urban forest comprehensive benefits had good consistency. At the target level, the comprehensive evaluation score of urban forests in the study area was 7.69. At the factor level, the weight value of the urban forest landscape structure was 0.675, and the comprehensive score was 7.62. The weight value of urban forest comprehensive benefits was 0.325, and the comprehensive score was 7.82. The quantitative weight value of urban forest greening in the study area was 0.6138, with a comprehensive score of 7.57. Based on the analysis of the issues in urban forests and ecological compensation in the research area of Tai’an City, corresponding ecological compensation strategies have been proposed. It is of great value to study the urban forest of Tai’an city, which can help to formulate more effective urban planning and sustainable development strategies. The research results can also provide a valuable reference and inspiration for the improvement of urban forest ecological environment and biodiversity protection in other areas.

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Section: Construction Of Comprehensive Benefit Evaluation Index Syste...mentioning

confidence: 99%

“…In recent years, environmental issues have received widespread attention, and ecological protection has become a key task [1]. Under the guidance of landscape design concepts in the new era, it is imperative to construct urban forests [2].…”

Section: Introductionmentioning

confidence: 99%

The Application of Geographic Information System in Urban Forest Ecological Compensation and Sustainable Development Evaluation

An,

Liu,

Hou

2024

Forests

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“…Saleh et al [20] proposed a stacked LSTM-based method to fuse the temporal features of different sensors, which achieved good results in driving behavior-classification problems. Han et al [21] employed a hybrid mechanism to design a fusion modelling method, which was utilized to accurately evaluate the energy consumption of smart buildings. Chen et al [22] proposed a deep-learning framework, based on a convolutional neural network (CNN) and a naive Bayes data-fusion scheme and then applied it to image detection.…”

Section: Data Fusionmentioning

confidence: 99%

Efficient Multi-Source Self-Attention Data Fusion for FDIA Detection in Smart Grid

Wang

Guo

et al. 2023

Symmetry

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As a new cyber-attack method in power cyber physical systems, false-data-injection attacks (FDIAs) mainly disturb the operating state of power systems by tampering with the measurement data of sensors, thereby avoiding bad-data detection by the power grid and threatening the security of power systems. However, existing FDIA detection methods usually only focus on the detection feature extraction between false data and normal data, ignoring the feature correlation that easily produces diverse data redundancy, resulting in the significant difficulty of detecting false-data-injection attacks. To address the above problem, we propose a multi-source self-attention data fusion model for designing an efficient FDIA detection method. The proposed data fusing model firstly employs a temporal alignment technique to integrate the collected multi-source sensing data to the identical time dimension. Subsequently, a symmetric hybrid deep network model is built by symmetrically combining long short-term memory (LSTM) and a convolution neural network (CNN), which can effectively extract hybrid features for different multi-source sensing data. Furthermore, we design a self-attention module to further eliminate hybrid feature redundancy and aggregate the differences between attack-data features and normal-data features. Finally, the extracted features and their weights are integrated to implement false-data-injection attack detection using a single convolution operation. Extensive simulations are performed over IEEE14 node test systems and IEEE118 node test systems; the experimental results demonstrate that our model can achieve better data fusion effects and presents a superior detection performance compared with the state-of-the-art.

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“…Comparing the hybrid model to its counterparts, the proposed hybrid model achieves maximum classification accuracy. Han et al 36 proposed a novel approach to model smart buildings to assess energy consumption based on the concept of physical-data fusion modeling (PFM). Ye et al 37 proposed a theoretical benchmark for optimizing the coordination of local electricity markets (LEM) using a system-centric model.…”

Section: Related Workmentioning

confidence: 99%

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Shaikh

Nazir

Khan

et al. 2022

Sci Rep

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Smart grids and smart homes are getting people’s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with “day as covariates” remained better than the 1, 2, 3, and 4-week scenarios.

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Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building

Cited by 13 publications

References 0 publications

The Application of Geographic Information System in Urban Forest Ecological Compensation and Sustainable Development Evaluation

The Application of Geographic Information System in Urban Forest Ecological Compensation and Sustainable Development Evaluation

Efficient Multi-Source Self-Attention Data Fusion for FDIA Detection in Smart Grid

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

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