Abstract
Forecasting the electrical appliance power consumption is a necessary and important part of the management of electrical power system, in order to assess people’s penchant for using electricity. Even though several studies are focused on forecasting building consumption, less attention is given to forecasting the use of single appliances. Indeed, some of the energy needs of consumers may be relatively delayed or anticipated to obtain a better consumption profile while maintaining consumer comfort. This paper focuses on forecasting appliance power consumption using a non-linear autoregressive (NAR) neural network model. The results obtained on the UK-DALE public dataset demonstrate that NAR models are suitable for forecasting of energy consumption with a good accuracy. The proposed model obtained the best Mean Absolute Errors, compared with the LSTM, Autoencoder, Combinatory optimization, FHMM, and Seq2point techniques.
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References
US Department of Energy (DOE) (2015) An Assessment of energy technologies and research—Chapter 1. Enabling Mod Electr Power Syst Technol Rev September:99
US Department of Energy (DOE) (2015) An Assessment of energy technologies and research—Chapter 5. Enabling Mod Electr Power Syst Technol Rev
Ruano A, Hernandez A, Ureña J, Ruano M, Garcia J (2019) NILM techniques for intelligent home energy management and ambient assisted living: a review. Energies 12(11):1–29
Zoha A, Gluhak A, Imran MA, Rajasegarar S (2012) Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors (Switzerland) 12(12):16838–16866
Barbato A, Capone A, Rodolfi M, Tagliaferri D (2011) Forecasting the usage of household appliances through power meter sensors for demand management in the smart grid. In: 2011 IEEE international conference on smart grid communications, SmartGridComm 2011, pp 404–409
Huber P, Gerber M, Rumsch A, Paice A (2018) Prediction of domestic appliances usage based on electrical consumption. Energy Inf 1(S1):
Abera FZ, Khedkar V (2020) Machine learning approach electric appliance consumption and peak demand forecasting of residential customers using smart meter data. Wirel Pers Commun 111(1):65–82
Hatami S, Pedram M (2010) Minimizing the electricity bill of cooperative users under a quasi-dynamic pricing model, pp 421–426
Ruiz L, Cuéllar M, Calvo-Flores M, Jiménez M (2016) An application of non-linear autoregressive neural networks to predict energy consumption in public buildings. Energies 9(9):684
Zhang F, Deb C, Lee SE, Yang J, Shah KW (2016) Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique. Energy Build 126:94–103
Fumo N, Rafe Biswas MA (2015) Regression analysis for prediction of residential energy consumption. Renew Sustain Energy Rev 47:332–343
Deb C, Eang LS, Yang J, Santamouris M (2016) Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks. Energy Build
Gul MS, Patidar S (2015) Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build
Ding N, Benoit C, Foggia G, Besanger Y, Wurtz F (2016) Neural network-based model design for short-term load forecast in distribution systems. IEEE Trans Power Syst
Motepe S, Hasan AN, Stopforth R (2019) Improving load forecasting process for a power distribution network using hybrid AI and deep learning algorithms. IEEE Access
Hong Y, Zhou Y, Li Q, Xu W, Zheng X (2020) A deep learning method for short-term residential load forecasting in smart grid. IEEE Access 8:55785–55797
Jia Y, Wang H, Batra N, Whitehouse K (2019) A tree-structured neural network model for household energy breakdown. In: Web Conference 2019—Proceedings of World Wide Web Conference WWW 2019, pp. 2872–2878 (2019)
Welikala S, Thelasingha N, Akram M, Ekanayake PB, Godaliyadda RI, Ekanayake JB (2019) Implementation of a robust real-time non-intrusive load monitoring solution. Appl Energy 238:1519–1529
Devlin MA, Hayes BP (2019) Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data. IEEE Trans Consum Electron 65(3):339–348
Çavdar IH, Faryad V (2019) New design of a supervised energy disaggregation model based on the deep neural network for a smart grid. Energies 12(7):
Fagiani M, Bonfigli R, Principi E, Squartini S, Mandolini L (2019) A non-intrusive load monitoring algorithm based on non-uniform sampling of power data and deep neural networks. Energies 12(7):
Machlev R, Belikov J, Beck Y, Levron Y (2019) MO-NILM: A multi-objective evolutionary algorithm for NILM classification. Energy Build
Kelly J, Knottenbelt W (2015) The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci Data
Zhang C, Zhong M, Wang Z, Goddard N, Sutton C (2018) Sequence-to-point learning with neural networks for non-intrusive load monitoring. In: 32nd AAAI conference on artificial intelligence. AAAI 2018:2604–2611
Ibrahim M, Jemei S, Wimmer G, Hissel D (2016) Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles. Electr Power Syst Res
Bengio Y, Simard P (1994) Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks
Lin T, Horne BG, Giles CL (1998) How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies. Neural Netw
Ruano AEB, Jones DI, Fleming PJ (1991) A new formulation of the learning problem for a neural network controller. In: 30th IEEE conference on decision and control, pp 865-866
Gao X, Li X, Zhao B, Ji W, Jing X, He Y (2019) Short-term electricity load forecasting model based on EMD-GRU with feature selection. Energies 12(6):1–18
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geosci Model Dev
Kim SH, Lee G, Kwon GY, Kim DI, Shin YJ (2018) Deep learning based on multi-decomposition for short-term load forecasting. Energies
Zico Kolter J, Johnson MJ (2011) REDD: a public data set for energy disaggregation research. In Proceedings of the SustKDD workshop on data mining applications in sustainability, San Diego, CA, USA, 21 Aug 2011
Anderson, K, Ocneanu A, Benitez D, Carlson D, Rowe A, Berges M (2012) BLUED: a fully labeled public dataset for event-based non-intrusive load monitoring research. In: Proceedings of the 2nd workshop on data mining applications in sustainability, Beijing, China, 12 Aug 2012
Gao J, Giri S, Kara EC, Bergés M (2014) PLAID: A public dataset of high-resolution electrical appliance measurements for load identification research. In: BuildSys 2014—Proceedings of the 1st ACM conference on embedded systems for energy-efficient buildings
Kelly J, Knottenbelt W (2015) Neural NILM: Deep neural networks applied to energy disaggregation. In: Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments, Seoul, Korea, pp 55–64, 4–5 November 2015
Batra N, Kelly J, Parson O, Dutta H, Knottenbelt W, Rogers A, Singh A, Srivastava M (2014) NILMTK: an open source toolkit for non-intrusive load monitoring. In: e-Energy 2014—Proceedings of the 5th ACM international conference on future energy systems
Acknowledgements
The authors would like to acknowledge the support of Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020 grant 01/SAICT/2018. Antonio Ruano also acknowledges the support of Fundação para a Ciência e Tecnologia grant UID/EMS/50022/2020, through IDMEC, under LAETA
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Laouali, I.H., Qassemi, H., Marzouq, M., Ruano, A., Dosse, S.B., El Fadili, H. (2022). A Non Linear Autoregressive Neural Network Model for Forecasting Appliance Power Consumption. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds) WITS 2020. Lecture Notes in Electrical Engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_69
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DOI: https://doi.org/10.1007/978-981-33-6893-4_69
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