IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v110y2013icp147-162.html
   My bibliography  Save this article

Long term forecasting of hourly electricity consumption in local areas in Denmark

Author

Listed:
  • Andersen, F.M.
  • Larsen, H.V.
  • Gaardestrup, R.B.

Abstract

Long term projections of hourly electricity consumption in local areas are important for planning of the transmission grid. In Denmark, at present the method used for grid planning is based on statistical analysis of the hour of maximum load and for each local area the maximum load is projected to change proportional to changes in the aggregated national electricity consumption. That is, specific local conditions are not considered. Yet, from measurements of local consumption we know that:

Suggested Citation

  • Andersen, F.M. & Larsen, H.V. & Gaardestrup, R.B., 2013. "Long term forecasting of hourly electricity consumption in local areas in Denmark," Applied Energy, Elsevier, vol. 110(C), pages 147-162.
    • Handle: RePEc:eee:appene:v:110:y:2013:i:c:p:147-162
    DOI: 10.1016/j.apenergy.2013.04.046
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261913003413
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2013.04.046?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
    2. Barten, A. P., 1969. "Maximum likelihood estimation of a complete system of demand equations," European Economic Review, Elsevier, vol. 1(1), pages 7-73.
    3. Räsänen, Teemu & Voukantsis, Dimitrios & Niska, Harri & Karatzas, Kostas & Kolehmainen, Mikko, 2010. "Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data," Applied Energy, Elsevier, vol. 87(11), pages 3538-3545, November.
    4. Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2005. "Modelling the hourly Spanish electricity demand," Economic Modelling, Elsevier, vol. 22(3), pages 551-569, May.
    5. Moazzami, M. & Khodabakhshian, A. & Hooshmand, R., 2013. "A new hybrid day-ahead peak load forecasting method for Iran’s National Grid," Applied Energy, Elsevier, vol. 101(C), pages 489-501.
    6. Dashti, Reza & Afsharnia, Saeed & Ghasemi, Hassan, 2010. "A new long term load management model for asset governance of electrical distribution systems," Applied Energy, Elsevier, vol. 87(12), pages 3661-3667, December.
    7. Matteo Manera & Angelo Marzullo, 2003. "Modelling the Load Curve of Aggregate Electricity Consumption Using Principal Components," Working Papers 2003.95, Fondazione Eni Enrico Mattei.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    2. El-Baz, Wessam & Tzscheutschler, Peter, 2015. "Short-term smart learning electrical load prediction algorithm for home energy management systems," Applied Energy, Elsevier, vol. 147(C), pages 10-19.
    3. Shao, Zhen & Gao, Fei & Zhang, Qiang & Yang, Shan-Lin, 2015. "Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting i," Applied Energy, Elsevier, vol. 156(C), pages 502-518.
    4. Boßmann, T. & Staffell, I., 2015. "The shape of future electricity demand: Exploring load curves in 2050s Germany and Britain," Energy, Elsevier, vol. 90(P2), pages 1317-1333.
    5. Andersen, F.M. & Larsen, H.V. & Juul, N. & Gaardestrup, R.B., 2014. "Differentiated long term projections of the hourly electricity consumption in local areas. The case of Denmark West," Applied Energy, Elsevier, vol. 135(C), pages 523-538.
    6. Lintao Yang & Honggeng Yang & Haitao Liu, 2018. "GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting," Sustainability, MDPI, vol. 10(1), pages 1-16, January.
    7. Martin Robinius & Felix ter Stein & Adrien Schwane & Detlef Stolten, 2017. "A Top-Down Spatially Resolved Electrical Load Model," Energies, MDPI, vol. 10(3), pages 1-16, March.
    8. Voulis, Nina & Warnier, Martijn & Brazier, Frances M.T., 2018. "Understanding spatio-temporal electricity demand at different urban scales: A data-driven approach," Applied Energy, Elsevier, vol. 230(C), pages 1157-1171.
    9. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    10. Jose Juan Caceres-Hernandez & Gloria Martin-Rodriguez & Jonay Hernandez-Martin, 2022. "A proposal for measuring and comparing seasonal variations in hourly economic time series," Empirical Economics, Springer, vol. 62(4), pages 1995-2021, April.
    11. Chatzisideris, Marios D. & Laurent, Alexis & Christoforidis, Georgios C. & Krebs, Frederik C., 2017. "Cost-competitiveness of organic photovoltaics for electricity self-consumption at residential buildings: A comparative study of Denmark and Greece under real market conditions," Applied Energy, Elsevier, vol. 208(C), pages 471-479.
    12. Knittel, Tamara & Palmer-Wilson, Kevin & McPherson, Madeleine & Wild, Peter & Rowe, Andrew, 2024. "Heating electrification in cold climates: Invest in grid flexibility," Applied Energy, Elsevier, vol. 356(C).
    13. Angreine Kewo & Pinrolinvic D. K. Manembu & Per Sieverts Nielsen, 2020. "Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model," Energies, MDPI, vol. 13(14), pages 1-28, July.
    14. F. M. Andersen & H. V. Larsen & L. Kitzing & P. E. Morthorst, 2014. "Who gains from hourly time‐of‐use retail prices on electricity? An analysis of consumption profiles for categories of Danish electricity customers," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(6), pages 582-593, November.
    15. Yukseltan, E. & Kok, A. & Yucekaya, A. & Bilge, A. & Aktunc, E. Agca & Hekimoglu, M., 2022. "The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey," Utilities Policy, Elsevier, vol. 76(C).
    16. Ali K k & Erg n Y kseltan & Mustafa Hekimo lu & Esra Agca Aktunc & Ahmet Y cekaya & Ay e Bilge, 2022. "Forecasting Hourly Electricity Demand Under COVID-19 Restrictions," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 73-85.
    17. Yu, Dongwei & Tan, Hongwei, 2016. "Application of ‘potential carbon’ in energy planning with carbon emission constraints," Applied Energy, Elsevier, vol. 169(C), pages 363-369.
    18. Dedinec, Aleksandra & Filiposka, Sonja & Dedinec, Aleksandar & Kocarev, Ljupco, 2016. "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Elsevier, vol. 115(P3), pages 1688-1700.
    19. Sanstad, Alan H. & McMenamin, Stuart & Sukenik, Andrew & Barbose, Galen L. & Goldman, Charles A., 2014. "Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning," Applied Energy, Elsevier, vol. 128(C), pages 265-276.
    20. Gerossier, Alexis & Barbier, Thibaut & Girard, Robin, 2017. "A novel method for decomposing electricity feeder load into elementary profiles from customer information," Applied Energy, Elsevier, vol. 203(C), pages 752-760.
    21. Tulin Guzel & Hakan Cinar & Mehmet Nabi Cenet & Kamil Doruk Oguz & Ahmet Yucekaya & Mustafa Hekimoglu, 2023. "A Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessing," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 179-193, September.
    22. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    23. Erik Dahlquist & Fredrik Wallin & Koteshwar Chirumalla & Reza Toorajipour & Glenn Johansson, 2023. "Balancing Power in Sweden Using Different Renewable Resources, Varying Prices, and Storages Like Batteries in a Resilient Energy System," Energies, MDPI, vol. 16(12), pages 1-28, June.
    24. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    25. Feng, Yonghan & Ryan, Sarah M., 2016. "Day-ahead hourly electricity load modeling by functional regression," Applied Energy, Elsevier, vol. 170(C), pages 455-465.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andersen, F.M. & Larsen, H.V. & Juul, N. & Gaardestrup, R.B., 2014. "Differentiated long term projections of the hourly electricity consumption in local areas. The case of Denmark West," Applied Energy, Elsevier, vol. 135(C), pages 523-538.
    2. F. M. Andersen & H. V. Larsen & L. Kitzing & P. E. Morthorst, 2014. "Who gains from hourly time‐of‐use retail prices on electricity? An analysis of consumption profiles for categories of Danish electricity customers," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(6), pages 582-593, November.
    3. Jose Juan Caceres-Hernandez & Gloria Martin-Rodriguez & Jonay Hernandez-Martin, 2022. "A proposal for measuring and comparing seasonal variations in hourly economic time series," Empirical Economics, Springer, vol. 62(4), pages 1995-2021, April.
    4. Barnett, William A. & Serletis, Apostolos, 2008. "Consumer preferences and demand systems," Journal of Econometrics, Elsevier, vol. 147(2), pages 210-224, December.
    5. Mazzocchi, Mario, 2006. "Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2191-2205, May.
    6. Keuzenkamp, Hugo A. & Barten, Anton P., 1995. "Rejection without falsification on the history of testing the homogeneity condition in the theory of consumer demand," Journal of Econometrics, Elsevier, vol. 67(1), pages 103-127, May.
    7. Laura Spierdijk & Sherrill Shaffer & Tim Considine, 2016. "Adapting to changing input prices in response to the crisis: The case of US commercial banks," CAMA Working Papers 2016-15, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Huffman, Wallace, 2004. "Marketizing U.S. Production in the Post-War Era: Implications for Estimating CPI Bias and Real Income from a Complete-Household-Demand System," Staff General Research Papers Archive 11987, Iowa State University, Department of Economics.
    9. Barnett, William A. & Serletis, Apostolos, 2008. "The Differential Approach to Demand Analysis and the Rotterdam Model," MPRA Paper 12319, University Library of Munich, Germany.
    10. Paris, Quirino & Caracciolo, Francesco, 2012. "Quantity Versus Shares in Estimating Demand Systems," Working Papers 124575, University of California, Davis, Department of Agricultural and Resource Economics.
    11. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    12. van Heeswijk, B J & de Boer, P M C & Harkema, R, 1993. "A Dynamic Specification of an AIDS Import Allocation Model," Empirical Economics, Springer, vol. 18(1), pages 57-73.
    13. Korir, Lilian & Rizov, Marian & Ruto, Eric, 2020. "Food security in Kenya: Insights from a household food demand model," Economic Modelling, Elsevier, vol. 92(C), pages 99-108.
    14. Moschini, G. & Moro, D., 1993. "A Food demand System for Canada," Papers 1-93, Gouvernement du Canada - Agriculture Canada.
    15. Seale, James L. & Solano, Alexis A., 2012. "The changing demand for energy in rich and poor countries over 25years," Energy Economics, Elsevier, vol. 34(6), pages 1834-1844.
    16. Shawna Grosskopf & Kathy Hayes, 1983. "Do Local Governments Maximize Anything?," Public Finance Review, , vol. 11(2), pages 202-216, April.
    17. Chavas, Jean-Paul, 2013. "On Demand Analysis and Dynamics: A Benefit Function Approach," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149683, Agricultural and Applied Economics Association.
    18. Holt, Matthew T., 2002. "Inverse demand systems and choice of functional form," European Economic Review, Elsevier, vol. 46(1), pages 117-142, January.
    19. Huang, Tai-Hsin & Lin, Chung-I & Chen, Kuan-Chen, 2017. "Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies," Pacific-Basin Finance Journal, Elsevier, vol. 41(C), pages 93-110.
    20. Adelaja, Adesoji & Hoque, Anwarul, 1985. "Estimating The Product Revenue Bias Of Technological Change," 1985 Annual Meeting, August 4-7, Ames, Iowa 278649, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:110:y:2013:i:c:p:147-162. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.