Energy Demand Analysis and Forecast

Sustainable energy systems are necessary to save the natural resources avoiding environmental impacts which would compromise the development of future generations. Delivering sustainable energy will require an increased efficiency of the generation process including the demand side. The architecture of the future energy supply can be characterized by a combination of conventional centralized power plants with an increasing number of distributed energy resources, including cogeneration and renewable energy systems. Thus efficient forecast tools are necessary predicting the energy demand for the operation and planning of power systems. The role of forecasting in deregulated energy markets is essential in key decision making, such as purchasing and generating electric power, load switching, and demand side management. This chapter describes the energy data analysis and the basics of the mathematical modeling of the energy demand. The forecast problem will be discussed in the context of energy management systems. Because of the large number of influence factors and their uncertainty it is impossible to build up an ‘exact’ physical model for the energy demand. Therefore the energy demand is calculated on the basis of statistical models describing the influence of climate factors and of operating conditions on the energy consumption. Additionally artificial intelligence tools are used. A large variety of mathematical methods and ideas have been used for energy demand forecasting (see Hahn et al., 2009, or Fischer, 2008). The quality of the demand forecast methods depends significantly on the availability of historical consumption data as well as on the knowledge about the main influence parameters on the energy consumption. These factors also determine the selection of the best suitable forecast tool. Generally there is no 'best' method. Therefore it is very important to proof the available energy data basis and the exact conditions for the application of the tool. Within this chapter the algorithm of the model building process will be discussed including the energy data treatment and the selection of suitable forecast methods. The modeling results will be interpreted by statistical tests. The focus of the investigation lies in the application of regression methods and of neural networks for the forecast of the power and heat demand for cogeneration systems. It will be shown that similar methods can be applied to both forecast tasks. The application of the described methods will be demonstrated by the heat and power demand forecast for a real district heating system containing different cogeneration units.