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

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  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. 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.
  6. 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.
  7. 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.
  8. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
  9. 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.
  10. 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).
  11. 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.
  12. 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).
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho & Acharya, Rajendra & Dinh, Toan, 2025. "Electricity demand uncertainty modeling with Temporal Convolution Neural Network models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 209(C).
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. Feng, Yonghan & Ryan, Sarah M., 2016. "Day-ahead hourly electricity load modeling by functional regression," Applied Energy, Elsevier, vol. 170(C), pages 455-465.
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