Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China

Liu, Yang; Chen, Hongyu; Zhang, Limao; Wu, Xianguo; Wang, Xianjia

doi:10.1016/j.jclepro.2020.122542

Cited by 150 publications

(45 citation statements)

References 31 publications

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“…In [16], for example, Walket et al applied several learning algorithmsboosted tree, random forest, support vector machine (SVM) and artificial neural networksto predict commercial building electricity demands. Liu et al [17] applied SVM to public buildings' energy consumption from Wuhan (China). In this case, the energy consumption data were combined with climatic and time-cycle factors.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

et al. 2022

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Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.

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Section: Related Workmentioning

confidence: 99%

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

et al. 2022

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“…As example, the work presented by Liu et al [32], where the authors presented a support vector machine (SVM) method to forecasting and diagnose public buildings energy consumption based on different input parameters, such as historical energy consumption data, climatic factors and time-cycle factors. The work was carried out on a dataset from city of Wuhan (China), and their results showed that they were able to detect that air conditioning energy consumption was abnormal for four days in September.…”

Section: Related Workmentioning

confidence: 99%

Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

Pinto-Roa¹,

Medina²,

Román³

et al. 2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

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The discovery and description of patterns in electric energy consumption time series is fundamental for timely management of the system. A bicluster describes a subset of observation points in a time period in which a consumption pattern occurs as abrupt changes or instabilities homogeneously. Nevertheless, the pattern detection complexity increases with the number of observation points and samples of the study period. In this context, current bi-clustering techniques may not detect significant patterns given the increased search space. This study develops a parallel evolutionary computation scheme to find biclusters in electric energy. Numerical simulations show the benefits of the proposed approach, discovering significantly more electricity consumption patterns compared to a state-of-the-art non-parallel competitive algorithm.

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“…Furthermore, computational approaches have shown limited prediction performance due to the non-stationarity nature and serious trends in the energy demand; therefore, many prediction models have been tested using machine learning methods to improve the forecasting quality [14]- [16]. For instance, Liu et al [17] have developed a support vector machine (SVM) model to forecast and analyze public buildings' energy consumption. Driven by the solid nonlinear supporting vector regression capacities, Chen et al [18] proposed a model that forecasts the electrical load based on the ambient temperature.…”

Section: Introductionmentioning

confidence: 99%

Household Energy Consumption Prediction Using the Stationary Wavelet Transform and Transformers

2022

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In this paper, we present a new method for forecasting power consumption. Household power consumption prediction is essential to manage and plan energy utilization. This study proposes a new technique using machine learning models based on the stationary wavelet transform (SWT) and transformers to forecast household power consumption in different resolutions. This approach works by leveraging selfattention mechanisms to learn complex patterns and dynamics from household power consumption data. The SWT and its inverse are used to decompose and reconstruct the actual and the forecasted household power consumption data, respectively, and deep transformers are used to forecast the SWT subbands. Experimental findings show that our hybrid approach achieves superior prediction performance compared to the existing power consumption prediction methods.INDEX TERMS Household power consumption, transformers, stationary wavelet transform, time series forecasting.

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Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China

Cited by 150 publications

References 31 publications

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

Household Energy Consumption Prediction Using the Stationary Wavelet Transform and Transformers

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