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General Research Article

Uni-Variate and Multi-Variate Short-Term Household Electricity Consumption Prediction Using Machine Learning Technique

Author(s): Sakshi Tyagi* and Pratima Singh

Volume 15, Issue 6, 2022

Published on: 04 October, 2021

Article ID: e041021196963 Pages: 11

DOI: 10.2174/2666255814666211004112725

Price: $65

Abstract

Background: Electricity consumption prediction plays an important role in conservation, development, and future planning. Accurate prediction model has various field applications in real-life scenarios, future electricity demand estimation, performance evaluation of current time, fault detection, efficient energy production, resource-saving, and many more. In this paper, a CNN based short term building electricity consumption prediction model is developed and tested for two different types of datasets that can perform weekly prediction. Two different datasets are used to check how the algorithm behaves on different datasets i.e., what are the impacts dataset has on prediction accuracy. Errors were calculated using MAE and RMSE.

Objective: The objective of the study is to develop an electricity consumption prediction (ECP) model for a univariate and multivariate dataset using CNN and LSTM network and to find that how the correlation and independency of features affect the electricity prediction task.

Methods: The proposed electricity consumption model is built using the deep CNN andLSTM network and is trained and tested using the univariate and multivariate time series dataset thus the two experiments have been performed and are named as U-ECPCL (Univariate- Electricity Consumption Prediction using CNN and LSTM) and M-ECPCL (Multivariate- Electricity Consumption Prediction using CNN and LSTM) respectively.

Results: The model predicts accurately with few errors with MAE of 0.251 and RMSE of 0.66 for univariate dataset and MAE of 4.36 and RMSE of 11.53 for a multivariate dataset.

Conclusion: The model predicts accurately with few errors and if the prediction error of univariate and multivariate are compared then it is concluded that the univariate model outperforms the multivariate model.

Keywords: CNN, LSTM, building electricity consumption prediction, univariate analysis, multivariate analysis, IEA.

Graphical Abstract

[1]
ExxonMobil. 2018 Outlook for Energy: A View to 2040. Available online: https://corporate.exxonmobil.com/en/~/media/Global/ Files/outlook-for-energy/2018-Outlook-for-Energy.pdf (Accessed on 25 March 2019).
[2]
"International Energy Agency. World Energy Outlook 2018; International Energy Agency Publications: Paris, France", 2018
[3]
M. Ledu, H.D. Matthews, and R. de Elía, "Regional estimates of the transient climate response to cumulative CO2 emissions. Nat. Clim. Chang", 2016 6, 474–478. [CrossRef]
[4]
Energy 2020, A Strategy for Competitive, Sustainable and Secure Energy. Available online: http://eur-lex. europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52010DC0639&from=EN (Accessed on 12 March 2019).
[5]
T.I.E. Agency, Energy Efficiency: Buildings. Available online: https://www.iea.org/topics/energyefficiency/ buildings (accessed on 20 March 2019).
[6]
HaiZhong, Jiajun Wang, HongjieJia, Yunfei Mu, ShileiLv, Vector field-based support vector regression for building energy consumption prediction, Applied Energy, Volume 242, 2019, Pages 403-414, ISSN 0306-2619.
[7]
SeyedAmirhosain Sharif, Amin Hammad, Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC and LCA, Journal of Building Engineering, Volume 25, 2019, 100790, ISSN 2352- 7102.
[8]
M. Fayaz, H. Shah, A.M. Aseere, W.K. Mashwani, and A.S. Shah, "A framework for prediction of household energy consumption using feed-forward back propagation neural network. Technologies",  , vol. 7, no. 2, p. 30, 2019.
[9]
Cheng Fan, Yongjun Sun, Yang Zhao, Mengjie Song, Jiayuan Wang, Deep learning-based feature engineering methods for improved building energy prediction, Applied Energy, Volume 240, 2019, Pages 35-45, ISSN 0306-2619.
[10]
M.Q. Raza, and A. Khosravi, "A review of artificial intelligence-based load demand forecasting techniques for smart grid and buildings", Renew. Sustain. Energy Rev., 2015. [50, 1352–1372. [CrossRef].
[11]
M. Muller, (2019) Creating building energy prediction models with convolutional recurrent neural networks, 31th Twente student conference on IT 5th, 2019, Enschede, The Netherlands.
[12]
JatinBedi, DurgaToshniwal, Deep learning framework to forecast electricity demand, Applied Energy, Volume 238, 2019, Pages 1312-1326, ISSN 0306-2619.
[13]
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors. Cognitive modeling, 5(3):1, 1988.
[14]
S. Hochreiter, and J. Schmidhuber, huber. Long short-term memory. Neural computation, 9(8):1735{1780, 1997.
[15]
B. Drury, Crawley, Linda K. Lawrie, Frederick C. Winkelmann, W.F. Buhl, Y.Joe Huang, Curtis O. Pedersen, Richard K. Strand, Richard J. Liesen, Daniel E. Fisher, Michael J. Witte, Jason Glazer, EnergyPlus: creating a new-generation building energy simulation program, Energy and Buildings, Volume 33, Issue 4, 2001, Pages 319-331, ISSN 0378-7788.
[16]
Yimin Zhu, Applying computer-based simulation to energy auditing: A case study, Energy and Buildings, Volume 38, Issue 5, 2006, Pages 421-428, ISSN 0378-7788.
[17]
D. Yan, J. Xia, and W. Tang, "DeST — An integrated building simulation toolkit Part I: Fundamentals", Build. Simul., vol. 1, pp. 95-110, 2008.
[http://dx.doi.org/10.1007/s12273-008-8118-8]
[18]
F. Wahid, and D.H. Kim, "Short-term energy consumption prediction in Korean residential buildings using optimized multi-layer perceptron", Kuwait J. Sci., vol. 44, no. 2, pp. 67-77, 2017.
[19]
DebadityaChakraborty&HazemElzarka (2018): , "Advanced machine learning techniques for building performance simulation: a comparative analysis", J. Build. Perform. Simul..
[http://dx.doi.org/10.1080/19401493.2018.1498538]
[20]
"SaiAbhilash Reddy Sangireddy, Aviruch Bhatia, and Vishal Garg, Development of a surrogate model by extracting top characteristic feature vectors for building energy prediction", J. Build. Eng..
[http://dx.doi.org/10.1016/j.jobe.2018.12.018]
[21]
"Fayaz, Muhammad Kim, DoHyeun. (2018).A Prediction Methodology of Energy Consumption Based on Deep Extreme Learning Machine and Comparative Analysis in Residential Buildings", Electronics (Basel), vol. 7, p. 222, 2018.
[http://dx.doi.org/10.3390/electronics7100222]
[22]
MehrnooshTorabi, "SattarHashemi, Mahmoud Reza Saybani, ShahaboddinShamshirband, Amir Mosavi, A hybrid clustering and classification technique for forecasting short-term energy consumption", Environ. Prog. Sustain. Energy, 2018.
[23]
A. Galicia, R. Talavera-Llames, A. Troncoso, I. Koprinska, and F. Martínez-Álvarez, Multi-step forecasting for big data time series based on ensemble learning, Knowledge-Based Systems, Volume 163, 2019, Pages 830-841, ISSN 0950-7051.
[24]
NJ Johannesen, M Kolhe, and M Goodwin, "Relative evaluation of regression tools for urban area electrical energy demand forecasting, Journal of Cleaner Production (2019),",
[http://dx.doi.org/10.1016/j.jclepro.2019.01.108]
[25]
T. Ahmad, and H. Chen, "Deep learning for multi-scale smart energy forecasting", Energy, 2019.
[http://dx.doi.org/10.1016/j.energy.2019.03.080]
[26]
"XiaodongXu, Wei Wang, Tianzhen Hong, Jiayu Chen, Incorporating machine learning with building network analysis to predict multi-building energy use, Energy & Buildings (2019),",
[http://dx.doi.org/10.1016/j.enbuild.2019.01.002]
[27]
Kadir Amasyali, Nora El-Gohary, Predicting Energy Consumption of Office Buildings: A Hybrid Machine Learning-Based Approach, Advances in Informatics and Computing in Civil and Construction Engineering, Print ISBN: 978-3-030-00219-0, Electronic ISBN: 978-3-030-00220-6, Copyright Year: 2019, Springer International Publishing.
[28]
Federico Divina& Miguel García Torres & Francisco A. Goméz Vela & José Luis VázquezNoguera 2019, "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings", Energies, MDPI, Open Access Journal, vol. 12, no. 10, pp. 1-23, . [May.
[29]
S.S. Roy, P. Samui, and I. Nagtode,
et al. J Ambient Intell Human Comput (2019). Forecasting heating and cooling loads of buildings: a comparative performance analysis, [http://dx.doi.org/10.1007/s12652-019-01317-y]
[30]
Ran Wang, Shilei Lu, Qiaoping Li, Multi-criteria comprehensive study on a predictive algorithm of hourly heating energy consumption for residential buildings, Sustainable Cities and Society, Volume 49, 2019, 101623, ISSN 2210-6707.
[31]
S. Ardabili, A. Mosavi, and A.R. Várkonyi-Kóczy,
Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods. Preprints 2019, 2019080203 [http://dx.doi.org/10.20944/preprints201908.0203.v1]
[32]
G. Hebrail, and A. Berard, Individual Household Electric Power Consumption Data Set. UCI Machine Learning Repository. Available online: https://archive.ics.uci.edu/ml/datasets/ individual+household+electric+power+consumption
[33]
Carmen Lai, Marcel J.T. Reinders, LodewykWessels, Random subspace method for multivariate feature selection, Pattern Recognition Letters, Volume 27, Issue 10, 2006, Pages 1067-1076, ISSN 0167-8655.
[34]
T. Chai, and R.R. Draxler, "Root mean square error (RMSE) or mean absolute error (MAE)?", Geosci. Model Dev. Discuss., vol. 7, pp. 1525-1534, 2014.
[http://dx.doi.org/10.5194/gmdd-7-1525-2014]

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