A comprehensive analytical exploration and customer behaviour analysis of smart home energy consumption data with a practical case study

Energy Reports - Tập 8 - Trang 9081-9093 - 2022
K. Purna Prakash1, Y.V. Pavan Kumar2, Ch. Pradeep Reddy1, D. John Pradeep2, Aymen Flah3, Ali Nasser Alzaed4, Ahmad Aziz Al Ahamdi5, Sherif S.M. Ghoneim5
1School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh 522237, India
2School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh 522237, India
3National Engineering School of Gabès, Processes, Energy, Environment and Electrical Systems, University of Gabès, LR18ES34, Gabes 6072, Tunisia
4Department of Architecture Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
5Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Tài liệu tham khảo

Afzalan, 2020, A machine learning framework to infer time-of-use of flexible loads: Resident behavior learning for demand response, IEEE Access, 8, 111718, 10.1109/ACCESS.2020.3002155

Al-Otaibi, 2016, Feature construction and calibration for clustering daily load curves from smart-meter data, IEEE Trans. Ind. Inform., 12, 645, 10.1109/TII.2016.2528819

Albert, 2013, Smart meter driven segmentation: What your consumption says about you, IEEE Trans. Power Syst., 28, 4019, 10.1109/TPWRS.2013.2266122

Buzau, 2019, Detection of non-technical losses using smart meter data and supervised learning, IEEE Trans. Smart Grid, 10, 2661, 10.1109/TSG.2018.2807925

Feng, 2020, Deep learning-based real-time building occupancy detection using AMI data, IEEE Trans. Smart Grid, 11, 4490, 10.1109/TSG.2020.2982351

Gianniou, 2018, Clustering-based analysis for residential district heating data, Energy Convers. Manage., 165, 840, 10.1016/j.enconman.2018.03.015

Guo, 2015, Home appliance load modeling from aggregated smart meter data, IEEE Trans. Power Syst., 30, 254, 10.1109/TPWRS.2014.2327041

Haben, 2016, Analysis and clustering of residential customers energy behavioral demand using smart meter data, IEEE Trans. Smart Grid, 7, 136, 10.1109/TSG.2015.2409786

Kezunovic, 2020, Big data analytics for future electricity grids, Electr. Power Syst. Res., 189, 10.1016/j.epsr.2020.106788

Khan, 2020, Smart meter data based load forecasting and demand side management in distribution networks with embedded PV systems, IEEE Access, 8, 2631, 10.1109/ACCESS.2019.2962150

Kim, 2019, Predicting residential energy consumption using CNN-LSTM neural networks, Energy, 182, 72, 10.1016/j.energy.2019.05.230

Kong, 2019, Short-term residential load forecasting based on LSTM recurrent neural network, IEEE Trans. Smart Grid, 10, 841, 10.1109/TSG.2017.2753802

Kong, 2018, An extensible approach for non-intrusive load disaggregation with smart meter data, IEEE Trans. Smart Grid, 9, 3362, 10.1109/TSG.2016.2631238

Kwac, 2014, Household energy consumption segmentation using hourly data, IEEE Trans. Smart Grid, 5, 420, 10.1109/TSG.2013.2278477

Mehrjerdi, 2019, Daily-seasonal operation in net-zero energy building powered by hybrid renewable energies and hydrogen storage systems, Energy Convers. Manage., 201, 10.1016/j.enconman.2019.112156

Pipattanasomporn, 2014, Load profiles of selected major household appliances and their demand response opportunities, IEEE Trans. Smart Grid, 5, 742, 10.1109/TSG.2013.2268664

Purna Prakash, 2022, Analytical approach to exploring the missing data behavior in smart home energy consumption dataset, J. Renew. Energy Environ. (JREE), 9, 37

Quilumba, 2015, Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities, IEEE Trans. Smart Grid, 6, 911, 10.1109/TSG.2014.2364233

Shaukat, 2021, Cluster analysis and model comparison using smart meter data, Sensors, 21, 3157, 10.3390/s21093157

Shi, 2018, Deep learning for household load forecasting—A novel pooling deep RNN, IEEE Trans. Smart Grid, 9, 5271, 10.1109/TSG.2017.2686012

Syed, 2021, Household-level energy forecasting in smart buildings using a novel hybrid deep learning model, IEEE Access, 9, 33498, 10.1109/ACCESS.2021.3061370

The tracebase data set [Online]. Available: http://www.tracebase.org/.

Wang, 2018, Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns, Energy Convers. Manage., 171, 839, 10.1016/j.enconman.2018.06.017

Welikala, 2019, Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting, IEEE Trans. Smart Grid, 10, 448, 10.1109/TSG.2017.2743760

Zhang, 2016, Economic and environmental scheduling of smart homes with microgrid: DER operation and electrical tasks, Energy Convers. Manage., 110, 113, 10.1016/j.enconman.2015.11.056