Abstract
The issue of appropriated range of electricity market price forecasting in the problem of enterprise’s electricity consumption expenses optimisation is considered. Various kinds of battery energy storage systems (BESS) for small enterprises, as well as control algorithms for energy consumption expenditures, cost reduction, are considered. The estimation of accuracy of hourly electricity price time series forecasting with the artificial neural networks (ANN) algorithm is made with step-by-step increasing depth of the forecast. It is shown that for the optimal control of electricity consumption forecast makes sense no more than for about of few hours (or tens hours) ahead only depending on the forecast errors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Payne, J.: Survey of the international evidence on the causal relationships between energy consumption and growth. J. Econ. Stud. 37(1), 53–59 (2010)
Varfolomejeva, R., Gavrilovs, A., Iļjina, I.: The regulation possibility of energy-intensive enterprises according to the market price change. In: 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe, Italy, Milan, 6–9 June 2017, pp. 1118–1123. IEEE, Piscataway (2017)
Nord Pool Spot market data. Electricity hourly prices. https://www.nordpoolgroup.com/Market-data1/Dayahead/Area-Prices/ALL1/Hourly/?. Accessed 13 Aug 2018
Xu, Y., Xie, L., Singh, C.: Optimal scheduling and operation of load aggregators with electric energy storage facing price and demand uncertainties. In: North American Power Symposium (NAPS), pp. 1–7 (2011)
Lebedev, D., Rosin, A.: Modelling of electricity spot price and load. In: Proceedings of 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), pp. 222–226. IEEE (2014)
Barnes, A., Balda, J., Geurin, S., Escobar-MejĂa, A.: Optimal battery chemistry, capacity selection, charge/discharge schedule, and lifetime of energy storage under time-of-use pricing. In: Proceedings of Innovative Smart Grid Technologies (ISGT Europe), 2nd IEEE PES International Conference and Exhibition, pp. 1–7 (2011)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis, Forecasting and Control (1976)
Yang, J.: Power System Short-term load forecasting. Thesis for Ph.D. degree. Germany, Darmstadt, Elektrotechnik und Informationstechnik der Technischen Universität, 139 p. (2006)
Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, Upper Saddle River, 938 p. (2009)
Mazengia, D.H.: Forecasting Spot Electricity Market Prices Using Time Series Models: Thesis for the Degree of Master of Science in Electric Power Engineering. Gothenburg, Chalmers University of Technology, 89 p. (2008)
Mitrea, C.A., Lee, C.K.M., Wu, Z.: A comparison between neural networks and traditional forecasting methods: a case study. Int. J. Eng. Bus. Manag. 1(2), 19–24 (2009)
Hagan, M.T.: Neural Network Design, 2nd edn. PWS, MA, 1012 p. (1996)
Acknowledgements
This research was granted by ERDF funding, project “Optimum planning of an energy-intensive manufacturing process and optimisation of its energy consumption depending on changes in the market price (2017–2019)”, No 1.1.1.1/16/A/280 (Subcontract No L-s-2017/12-9).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Krivchenkov, A., Grakovski, A., Balmages, I. (2019). Required Depth of Electricity Price Forecasting in the Problem of Optimum Planning of Manufacturing Process Based on Energy Storage System (ESS). In: Kabashkin, I., Yatskiv (Jackiva), I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2018. Lecture Notes in Networks and Systems, vol 68. Springer, Cham. https://doi.org/10.1007/978-3-030-12450-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-12450-2_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12449-6
Online ISBN: 978-3-030-12450-2
eBook Packages: EngineeringEngineering (R0)