Long-term electricity demand forecasting via ordinal regression analysis: the case of GreeceLong-term electricity demand forecasting via ordinal regression analysis: the case of Greece Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2018-04-252017enElectricity demand forecasting constitutes a critical process in the operation and planning procedures of power networks that highly affects the decisions of utility providers and energy policy makers. Accurate forecasting is vital in reducing costs, related to excess electricity storage and infrastructures, and achieving enhanced power security and stability. A novel modeling approach for long-term electricity demand forecasting is introduced via the application of ordinal regression analysis. Annual forecasts of the total net electricity demand in the Greek interconnected power system are provided for the years 2016-2025. The Gross Domestic Product (GDP) has been identified as the greatest influential parameter on the evolution of electricity demand. Furthermore, the forecasting model has achieved a minimum Mean Absolute Percentage Error (MAPE) of 2.14%. The extracted forecasts indicate a constant increase of the total net electricity demand in Greece as a result of the expected economic growth during the upcoming years. http://creativecommons.org/licenses/by/4.0/IEEE Manchester PowerTech2017 IEEE Manchester PowerTech Angelopoulos Dimitrios Psarras John E. Siskos, Yannis Institute of Electrical and Electronics Engineers Electricity demand forecasting Greece Non-linear optimization Ordinal regression