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http://dx.doi.org/10.3837/tiis.2021.06.001

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption  

Balachander, K (Department of Computer Science and Engineering, Velammal Institute of Technology Panchetti)
Paulraj, D (Department of Computer Science and Engineering, R.M.K College of Engineering and Technology Puduvoyal)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.6, 2021 , pp. 1957-1980 More about this Journal
Abstract
The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.
Keywords
Behavioral Analytics; Big Data Mining; Clustering Analysis; CNN; Energy Consumption; Energy Prediction; LSTM;
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1 S. Singh, and A. Yassine, "Big data mining of energy time series for behavioral analytics and energy consumption forecasting," Energies, 11(2), pp.452, 2018.   DOI
2 A. Rahman, V. Srikumar, and A.D. Smith, "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, 212, pp.372-385, 2018.   DOI
3 T.T. Nguyen, P.Krishnakumari, S.C. Calvert, H.L. Vu, and H. Van Lint, "Feature extraction and clustering analysis of highway congestion," Transportation Research Part C: Emerging Technologies, 100, pp.238-258, 2019.   DOI
4 A.K. Ozcanli, F. Yaprakdal, and M.Baysal, "Deep learning methods and applications for electrical power systems: A comprehensive review," International Journal of Energy Research, 44(9), pp. 7136-7157, 2020.   DOI
5 Jui-Sheng Chou, and Duc-Son Tran, "Forecasting Energy Consumption Time Series using Machine Learning Techniques based on Usage Patterns of Residential Householders," Energy, 165, pp. 709-726, 2018.
6 Y. Zhou, H. Wu, Q. Luo, and M. Abdel-Baset, "Automatic data clustering using nature-inspired symbiotic organism search algorithm," Knowledge-Based Systems, 163, pp.546-557, 2019.   DOI
7 E.L. Lydia, P.K. Kumar, K. Shankar, S.K. Lakshmanaprabu, R.M. Vidhyavathi, and A. Maseleno, "Charismatic document clustering through novel K-Means non-negative matrix factorization (KNMF) algorithm using key phrase extraction," International Journal of Parallel Programming, 48(3), pp.496-514, 2020.   DOI
8 C. Yuan, and H. Yang, "Research on K-value selection method of K-means clustering algorithm," J-Multidisciplinary Scientific Journal, 2(2), pp.226-235, 2019.   DOI
9 J. Kong, J. Han, J. Ding, H. Xia, and X. Han, "Analysis of students' learning and psychological features by contrast frequent patterns mining on academic performance," Neural Computing and Applications, 32(1), pp.205-211, 2020.   DOI
10 H.I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P.A. Muller, "Deep learning for time series classification: a review," Data Mining and Knowledge Discovery, 33(4), pp.917-963, 2019.   DOI
11 A. Shrestha and A. Mahmood, "Review of Deep Learning Algorithms and Architectures," IEEE Access, 7, pp. 53040-53065, 2019.   DOI
12 D. Gilboa, B. Chang, M. Chen, G. Yang, S.S. Schoenholz, E.H. Chi, and J. Pennington, "Dynamical isometry and a mean field theory of LSTMs and GRUs," arXiv preprint arXiv:1901.08987, 2019.
13 P. Huber, P. Schmieder, M. Gerber, and A. Rumsch, "Poster abstract: Is the run-time of domestic appliances predictable?," Computer Science-Research and Development, 33(1-2), pp.241-243, 2018.
14 F. Divina, M. Garcia Torres, F.A. Gomez Vela, and J.L.VazquezNoguera, "A comparative study of time series forecasting methods for short term electric energy consumption prediction in smart buildings," Energies, 12(10), pp.1934, 2019.   DOI
15 A.A. Alola, F.V. Bekun, andS.A. Sarkodie, "Dynamic impact of trade policy, economic growth, fertility rate, renewable and non-renewable energy consumption on ecological footprint in Europe," Science of the Total Environment, 685, pp. 702-709, 2019.   DOI
16 K. Zhou, and S. Yang, "Understanding household energy consumption behavior: The contribution of energy big data analytics," Renewable and Sustainable Energy Reviews, 56, pp.810-819, 2016.   DOI
17 V.K. Verma, and B. Chandra, "An application of theory of planned behavior to predict young Indian consumers' green hotel visit intention," Journal of Cleaner Production, 172, pp.1152-1162, 2018.   DOI
18 Y. Wang, W. Liao, and Y. Chang, "Gated recurrent unit network-based short-term photovoltaic forecasting," Energies, 11(8), pp.2163, 2018.   DOI
19 C. Ma, J.H. Menke, J. Dasenbrock, M. Braun, M. Haslbeck, and K.H. Schmid, "Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation," Applied Energy, 256, pp.113907, 2019.   DOI
20 A. Yassine, S. Singh, and A. Alamri, "Mining human activity patterns from smart home big data for health care applications," IEEE Access, 5, pp.13131-13141, 2017.   DOI
21 G. Bode, T. Schreiber, M. Baranski, and D. Muller, "A time series clustering approach for Building Automation and Control Systems," Applied Energy, 238, pp.1337-1345, 2019.   DOI
22 F. Ziel, and R. Steinert, "Probabilistic mid-and long-term electricity price forecasting," Renewable and Sustainable Energy Reviews, 94, pp.251-266, 2018.   DOI
23 S. Bouktif, A. Fiaz, A. Ouni, and M.A.Serhani, "Single and multi-sequence deep learning models for short- and medium-term electric load forecasting," Energies, 12(1), pp.149, 2019.   DOI
24 Q. Ding, W. Cai, C. Wang, and M. Sanwal, "The relationships between household consumption activities and energy consumption in china-an input-output analysis from the lifestyle perspective," Applied Energy, 207, pp.520-532, 2017.   DOI
25 C. Chatfield, and H. Xing, The analysis of time series: an introduction with R, CRC press, 2019.
26 J.Q. Wang, Y. Du, andJ. Wang, "LSTM based long-term energy consumption prediction with periodicity," Energy, 197, pp.117197, 2020.   DOI
27 S. Wang, J. Lian, Y. Peng, B. Hu, and H. Chen, "Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China," Agricultural Water Management, 221, pp.220-230, 2019.   DOI
28 M. Fahim, K. Fraz, and A. Sillitti, "TSI: Time Series to Imaging based Model for Detecting Anomalous Energy Consumption in Smart Buildings," Information Sciences, 523, pp.1-13, 2020.   DOI
29 L.G.B. Ruiz, M.C. Pegalajar, R. Arcucci, and M. Molina-Solana, "A Time-Series Clustering Methodology for Knowledge Extraction in Energy Consumption Data," Expert Systems with Applications, 160, pp.113731, 2020.   DOI
30 C.W. Song, H. Jung, and K. Chung, "Development of a medical big-data mining process using topic modeling," Cluster Computing, 22(1), pp.1949-1958, 2019.   DOI
31 S. Singh, and A. Yassine, "Mining energy consumption behavior patterns for households in smart grid," IEEE Transactions on Emerging Topics in Computing, 7(3), pp.404-419, 2019.   DOI
32 Y. Liu, C. Gong, L. Yang, and Y. Chen, "DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction," Expert Systems with Applications, 143, pp.113082, 2020.   DOI
33 L. Faes, A. Porta, M. Javorka, and G.Nollo, "Efficient computation of multiscale entropy over short biomedical time series based on linear state-space models," Complexity, 2017.
34 P. Wang, P, H. Zhang, Z. Qin, and G. Zhang, "A novel hybrid-Garch model based on ARIMA and SVM for PM2. 5 concentrations forecasting," Atmospheric Pollution Research, 8(5), pp.850-860, 2017.   DOI
35 J. Gu, Z. Wang, J. Kuen, L. Ma, A.Shahroudy, B. Shuai, andT. Chen, "Recent advances in convolutional neural networks," Pattern Recognition, 77, pp.354-377, 2018.   DOI
36 M. Kraus, and S.Feuerriegel, "Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences," Decision Support Systems, 125, pp.113100, 2019.   DOI
37 L. Mou, P. Ghamisi, and X. Zhu, "Deep recurrent neural networks for hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, 55(7), pp.3639-3655, 2017.   DOI
38 Z. Che, S. Purushotham, K. Cho, D. Sontag, and Y. Liu, "Recurrent neural networks for multivariate time series with missing values," Scientific Reports, 8(1), pp.1-12, 2018.
39 T.Y. Kim, and S.B. Cho, "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, 182, pp.72-81, 2019.   DOI
40 S.N. Fallah, R.C. Deo, M. Shojafar, M. Conti, and S.Shamshirband, "Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions," Energies, 11(3), pp.596, 2018.   DOI
41 A. Harell, S. Makonin, and I.V. Bajic, "A recurrent neural network for multisensory non-intrusive load monitoring on a Raspberry Pi," in Proc. of IEEE MMSP, 18, August 2018.