Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
The Methods and Approaches of Explainable Artificial Intelligence Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Description of Electricity Consumption by Using Leading Hours Intra-day Model

Authors : Krzysztof Karpio, Piotr Łukasiewicz, Rafik Nafkha, Arkadiusz Orłowski

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper focuses on parametrization of one-day time series of electricity consumption. In order to parametrize such time series data mining technique was elaborated. The technique is based on the multivariate linear regression and is self-configurable, in other words a user does not need to set any model parameters upfront. The model finds the most essential data points whose values allow to model the electricity consumptions for remaining hours in the same day. The number of data points required to describe the whole time series depends on the demanded precision which is up to the user. We showed that the model with only four describing variables, describes 20 remaining hours very well, exhibiting dominant relative error about 1.5%. It is characterized by a high precision and allows finding non-typical days from the electricity demand point of view.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Endogenous Factors Affecting the Cost of Large-Scale Geo-Stationary Satellite Systems
next chapter The Problem of Tasks Scheduling with Due Dates in a Flexible Multi-machine Production Cell
Literature
1.
2.
Khatoon, S., Ibraheem, Arunesh, Kr.S., Priti: Effects of various factors on electric load forecasting: an overview. In: 6th IEEE Power India International Conference (PIICON), Delhi, pp. 1–5 (2014)
3.
Kremers, E.: Modelling and Simulation of Electrical Energy Systems through a Complex Systems Approach using Agent-Based Models. KIT Scientific Publishing, Karlsruhe (2013)
4.
Neng, S., Jingjing, Z., Wenjie, Z.: Energy efficiency measures and convergence in China. Taking into account the effects of environmental and random factors. Pol. J. Environ. Stud. 24(1), 257–267 (2015)
5.
Li, J., Yang, L., Long, H.: Climatic impacts on energy consumption: Intensive and extensive margins. Energy Econ. 71, 332–343 (2018)CrossRef
6.
McCulloch, J., Ignatieva, K.: Forecasting high frequency intra-day electricity demand using temperature. SSRN Electron. J. (2017)
7.
Xie, J., Hong, T.: Wind speed for load forecasting models. Sustainability 9(5), 795 (2017)CrossRef
8.
Kalimoldayev, M., Drozdenko, A., Koplyk, I., Abdildayeva, M.A., Zhukabayeva, T.: Analysis of modern approaches for the prediction of electric energy consumption. Open Eng. 10, 350–361 (2020)CrossRef
9.
Manowska, A.: Using the LSTM Network to forecast the demand for Electricity in Poland. Appl. Sci. 10, 8455 (2020)CrossRef
10.
Del Real, A.J., Dorado, F., Duran, J.: Energy demand forecasting using deep learning: applications for the French grid. Energies 13, 2242 (2020)CrossRef
11.
Kang, T., Lim, D.Y., Tayara, H., Chong, K.T.: Forecasting of power demands using deep learning. Appl. Sci. 20, 7241 (2020)CrossRef
12.
Ciechulski, T., Osowski, S.: Deep learning approach to power demand forecasting in Polish power system. Energies 13, 6154 (2020)CrossRef
13.
Elias Barrón, I., et al.: Genetic algorithm with radial basis mapping network for the electricity consumption modeling. Appl. Sci. 10, 4239 (2020)CrossRef
14.
Gajowniczek, K., Nafkha, R., Ząbkowski, T.: Seasonal peak demand classification with machine learning techniques. In: 2018 International Conference on Applied Mathematics & Computer Science (ICAMCS), pp. 101–1014 (2018)
15.
Dudek, G.: Neural networks for pattern-based short-term load forecasting: a comparative study. Neurocomputing 205, 64–74 (2016)CrossRef
16.
Dudek, G.: Pattern-based local linear regression models for short-term load forecasting. Electr. Power Syst. Res. 130, 139–147 (2016)CrossRef
17.
Fan, G.F., Qing, S., Wang, H., Hong, W.C., Li, H.J.: Support vector regression model based on empirical mode decomposition and auto regression for electric load forecasting. Energies 6, 1887–1901 (2013)CrossRef
18.
Divina, F., Torres, M.G., Vela, F.A.G., Noguera, J.L.V.: A comparative study of time series forecasting methods for short term electric energy consumption prediction in smart buildings. Energies 12, 1934 (2019)CrossRef
19.
De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22, 443–473 (2006)CrossRef
20.
Halepoto, I.A., Uqaili, M.A., Chowdhry, B.S.: Least square regression based integrated multi-parameteric demand modeling for short term load forecasting. Mehran Univ. Res. J. Eng. Technol. 33(2), 215–226 (2014)
21.
Vu, D.H., Muttaqi, K.M., Agalgaonkar, A.P.: A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables. Appl. Energy 140, 385–394 (2015)CrossRef
22.
Ghandoor, A., Samhouri, M.: Electricity consumption in the industrial sector of Jordan: application of multivariate linear regression and adaptive neuro-fuzzy techniques. Jordan J. Mech. Ind. Eng. 3(1), 69–76 (2009)
23.
24.
Metadata
Title
Description of Electricity Consumption by Using Leading Hours Intra-day Model
Authors
Krzysztof Karpio
Piotr Łukasiewicz
Rafik Nafkha
Arkadiusz Orłowski
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77969-6

Electronic ISBN: 978-3-030-77970-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77970-2_30

Premium Partner

    Image Credits