# Electricity Usage Efficiency and Electricity Demand Modeling in the Case of Germany and the UK
## 1 Introduction  
During the last decade, there has been a heated discussion going on in Europe regarding the future consumption of electricity in the member countries European Union (EU). However, the analysis performed in various reports and publications offered very different views on future power consumption development. The disagreements are probably best exemplified by the following ministerial statements of Germany and the United Kingdom:  
* \"By 2050, electricity consumption is to drop by 25% compared to 2008, and should already be down 10% by 2020\" [1];
* \"In the longer term, by 2050, electricity demand is set to double, as we shift more transport and heating onto the electricity grid\" [2]. The 2050 mark is too far away to forecast reliably, but also much shorter horizons were usually associated with very different views on power consumption developments. German government plans to decrease consumption by 10% until 2020 have been in sharp contrast with most of the other forecasts performed for different European markets [3]. The analysis in this paper was performed to establish realistic expectations regarding future power consumption in Germany and the UK as the largest electricity markets in the EU [4]. For these purposes, the linear regression model was developed, which considered crucial variables affecting electricity demand and establishing their effect on the final power consumption based on the 2005-2013 period. Modeled future consumption forecasts in Germany were made until the year 2020. Now, five years later, it is also possible to establish the performance of the model based on the real electricity consumption data and evaluate what this means for the expectations regarding power consumption development in Germany during the next periods. The tendency today is to use more and more renewable energy sources, but care must be taken to ensure that the produced resources are properly used in the form of electricity [5,6]. New energy sources produce only a small fraction of the world's total energy. This share should be increased significantly in the future as there are fewer non-renewable energy sources and their reserves are running low, and their detrimental impact has been increasingly pronounced in recent years. The sun, without which there would be no life on our planet, gives the Earth several thousand times more energy than humanity manages to consume at the current stage of development. It speaks in favor of the fact that renewable sources can and must be put to better use and that if we act wisely, we do not have to worry about energy fossil fuels. Energy efficiency has a big impact, so today bulbs that used to be around 100 w perform their function at 10 w. Other appliances used in households and industries also consume much less energy than before. In addition to applying renewable energy, today many authors are concerned with predicting electricity consumption [7]. Frequent research deals with the topic of generating different methods of research on the prediction of electricity consumption [8, 9, 10]. Certainly, the amount of electricity consumed is increasing, but taking into account the contribution of renewable energy sources and energy efficiency, the total resulting consumption will be reduced by these impacts. A novel nature-inspired, new meta-heuristic algorithm called Multi-Verse Optimizer (MVO) was employed to solve the optimal frequency regulation problem and to show the effectiveness of the proposed control strategy [8]. A fractional-order proportional-integral-derivative (FOPID) controller is investigated for a three-level inverter called Multi-Neutral Point (MNP). This method is based on Multi-objective genetic algorithms, and the main objectives are to remove the amplitude error, the phase shift error, and to decrease the total harmonic distortion of the current [9]. These methods are based on artificial intelligence and could certainly be applied in the case of predicting electricity consumption. However, the authors opted for the statistical model.