Electricity Usage Efficiency and Electricity Demand Modeling in the Case of Germany and the UK

In this article, monthly and yearly electricity consumption predictions for the German power market were calculated using the multiple variable regression model. This model accounts for several factors that are often neglected when forecasting electricity demand in practice, in particular the role of the higher efficiency of electricity usage from year to year. The analysis performed in this paper helps to explain why no growth in power consumption has been observed in Germany during the last decade. It shows that the electricity efficiency usage dataset is a relevant input for the model, which mitigates the combined impact of other factors on the final electricity consumption. The electricity demand forecasting model presented in this article was built in the year 2013 with forecasts for the future years’ electricity demand in Germany provided until 2020. These forecasts and related findings are also evaluated in this article.

[1]  Kyungku Kim,et al.  Integrated Model of Economic Generation System Expansion Plan for the Stable Operation of a Power Plant and the Response of Future Electricity Power Demand , 2018, Sustainability.

[2]  Julien Jacques,et al.  Short-Term Electricity Demand Forecasting Using a Functional State Space Model , 2018 .

[3]  Eric Croiset,et al.  Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables , 2009 .

[4]  Lianming Zhao,et al.  Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator , 2018, Symmetry.

[5]  S. M. Al-Alawi,et al.  Principles of electricity demand forecasting. II. Applications , 1997 .

[6]  Saleh M. Al-Alawi,et al.  Principles of electricity demand forecasting. I. Methodologies , 1996 .

[7]  Dietmar Lindenberger,et al.  The role of grid extensions in a cost-efficient transformation of the European electricity system until 2050 , 2013 .

[8]  V. Bianco,et al.  Electricity consumption forecasting in Italy using linear regression models , 2009 .

[9]  O. Wagner,et al.  Surviving the Energy Transition: Development of a Proposal for Evaluating Sustainable Business Models for Incumbents in Germany’s Electricity Market , 2020, Energies.

[10]  B. Dudić,et al.  The Role of Residual Demand in Electricity Price Analysis and Forecasting: Case of Czech Electricity Market , 2017 .

[11]  P. Bodger,et al.  Forecasting electricity consumption in New Zealand using economic and demographic variables , 2005 .

[12]  Robin Girard,et al.  Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges , 2018, Energies.

[13]  D. Toke UK Electricity Market Reform—revolution or much ado about nothing? , 2011 .

[14]  L. Suganthi,et al.  Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand , 2018 .

[15]  Idiano D’Adamo,et al.  A Structured Literature Review on Obsolete Electric Vehicles Management Practices , 2019 .

[16]  S. Beck,et al.  Using regression analysis to predict the future energy consumption of a supermarket in the UK , 2014 .

[17]  C. Lutz,et al.  Green jobs? Economic impacts of renewable energy in Germany , 2012 .

[18]  E. Georgopoulou,et al.  Models for mid-term electricity demand forecasting incorporating weather influences , 2006 .

[19]  J. López-Gutiérrez,et al.  Foundations in Offshore Wind Farms: Evolution, Characteristics and Range of Use. Analysis of Main Dimensional Parameters in Monopile Foundations , 2019 .

[20]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .