Forecasting of power demand plays an essential role in the electric industry, as it provides the basis for making decisions in power system planning and operation. A great variety of methods for predicting electricity demand are being used by electrical companies, which are applicable to short-term, medium-term or long-term forecasting. But changing the electrical demand arises from complex interactions between personal, corporative and socio-economic factors. In such a dynamic environment ordinary forecasting techniques are not sufficient, and more sophisticated methods are needed. The objective is to effectively untangle all the factors that lead to demand change and to determine the underlying causes. But analyzing multiple personal and social factors is complicated, to say the least. We need both rich data, along with complex predictive models to analyze it.