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

Balachander, K; Paulraj, D

doi:10.3837/tiis.2021.06.001

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

KSII Transactions on internet and information systems : TIIS, v.15 no.6, 2021년, pp.1957 - 1980

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)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (19)

표 Table 1. Related works on electric energy consumption prediction
그림 Fig. 1. Proposed Model Flow
표 Table 2. Source Database for Frequent Pattern Mining
그림 Fig. 2. Possible Clustering between the Appliance
표 Table 3. Clustering Source Database-I
표 Table 4. Clustering Source Database-II
그림 Fig. 3. K-means Clustering Algorithm Flow Diagram
그림 Fig. 4. Unfolding during the RNN network estimation
그림 Fig. 5. Data flow in the RNN LSTM block
그림 Fig. 6. Information flow in GRU block
그림 Fig. 7. Energy utilization of appliance-day during the week
표 Table 5. Appliance - day during the week
그림 Fig. 8. Appliance-time associations @ hour of day
그림 Fig. 9. Number of clusters discovered vs dataset mining.
그림 Fig. 10. Home 4 energy consumption prediction vs. actual energy
표 Table 6. Prediction model accuracy
그림 Fig. 11. Comparison of prediction accuracy
표 Table 7. Prediction accuracy @ household level
표 Table 8. Performance analysis

AI 본문요약
AI-Helper

문제 정의

This research describes the influence of consumers' behavior and their particular preferences on reasons to use energy that can be taken from appliances time associated with time series for energy

성능/효과

1) To estimate electricity, use in real residential homes, a network of RNN-GRUs of a stable rating of accuracy 98.7 % was proposed.
2) In all cases of minute, hourly, daily and weekly unit resolution, our model forecasts complex electricity consumption with the highest level of performance compared to other methods.
3) Analyzing the method proposed and finding water pump, washing machine and air conditioner variables that exhibit the greatest impact on the forecast model.
In this research, the residential buildings' energy use in building 4 is projected to be reasonable between 8.2 and 61.8 kW
The proposed model is evaluated using genuine data sets for valuable energy scheduling. It was also noticed that the CNN, LSTM, and GRU networks were provided a deep learning ability. This approach to deep learning can be used to forecast the potential use of resources with greater detailed comparison and various models.
3954, the proposed model of RNN-GRU had a significant prediction limit on the other technique. Per year between 2017 and 2020 the expected efficiency of power usage rises by 9.27 percent. In this research, the residential buildings' energy use in building 4 is projected to be reasonable between 8.
This paper also introduced incremental frequent mining and estimation models by k-means clustering. Qualitative and quantitative research using time periods like 15 minutes, 30 minutes, 1 hour, and 12 hours have revealed results that actively support our approach to constant mining. The proposed model is evaluated using genuine data sets for valuable energy scheduling.
The performance metrics like RMSE and MAE respectively 0.512857 and 0.3954, the proposed model of RNN-GRU had a significant prediction limit on the other technique. Per year between 2017 and 2020 the expected efficiency of power usage rises by 9.
Qualitative and quantitative research using time periods like 15 minutes, 30 minutes, 1 hour, and 12 hours have revealed results that actively support our approach to constant mining. The proposed model is evaluated using genuine data sets for valuable energy scheduling. It was also noticed that the CNN, LSTM, and GRU networks were provided a deep learning ability.
This approach to deep learning can be used to forecast the potential use of resources with greater detailed comparison and various models. The results show that the model RNN-GRU efficiently and steadily predicts residential electricity demand and shows the good performance compared with current standards. The variable that affects the forecast is also explained.
It is also intended to develop in the future and learn without ceasing about big data from the timing of energy from individual homes. This enables providers to perform an online/nonstop vitality assessment and directly attract consumers after changes in uses are known by wonderful industries, which preserve their energy.
These examples can promote realistic, cost-saving plans for purchasers, gracefully balance vitality and ask for advances in reservation and assignment, plan vitality purchasing arrangements and maintenance schedules, and more thoroughly coordinate the skilled formation and essential arrangements of the system. This paper also introduced incremental frequent mining and estimation models by k-means clustering. Qualitative and quantitative research using time periods like 15 minutes, 30 minutes, 1 hour, and 12 hours have revealed results that actively support our approach to constant mining.

후속연구

Further, the proposed model is evaluated and is trying to make it realistic by bringing in real-time big data mining of time series from multiple houses. This will assist the energy providers to develop real time online energy prediction solutions to manage better for the dynamic smart grid Techniques.
In future, to learn and develop from significant numbers from the time series of energy big data from individual homes was proposed. It is also intended to develop in the future and learn without ceasing about big data from the timing of energy from individual homes. This enables providers to perform an online/nonstop vitality assessment and directly attract consumers after changes in uses are known by wonderful industries, which preserve their energy.

참고문헌 (41)

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.

상세보기
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.

상세보기
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.

상세보기
F. Ziel, and R. Steinert, "Probabilistic mid-and long-term electricity price forecasting," Renewable and Sustainable Energy Reviews, 94, pp.251-266, 2018.

상세보기
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.

상세보기
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.

상세보기
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.

상세보기
C. Chatfield, and H. Xing, The analysis of time series: an introduction with R, CRC press, 2019.
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.
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.

상세보기
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.

상세보기
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.
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.
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.

상세보기
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.
T.Y. Kim, and S.B. Cho, "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, 182, pp.72-81, 2019.
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.

상세보기
M. Kraus, and S.Feuerriegel, "Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences," Decision Support Systems, 125, pp.113100, 2019.

상세보기
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.

상세보기
Y. Wang, W. Liao, and Y. Chang, "Gated recurrent unit network-based short-term photovoltaic forecasting," Energies, 11(8), pp.2163, 2018.

상세보기
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.

상세보기
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.

상세보기
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.
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.
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.

상세보기
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.
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.
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.

상세보기
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.

상세보기
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.

상세보기
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.

상세보기
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.
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.

상세보기
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.

상세보기
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.

상세보기
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.

상세보기
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.

상세보기
A. Shrestha and A. Mahmood, "Review of Deep Learning Algorithms and Architectures," IEEE Access, 7, pp. 53040-53065, 2019.

상세보기
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.
J.Q. Wang, Y. Du, andJ. Wang, "LSTM based long-term energy consumption prediction with periodicity," Energy, 197, pp.117197, 2020.
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.

상세보기

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증