—In terms of power sector restructuring and electricity market opening there have been significant changes in the functioning of activities at the level of Public Enterprise Elektroprivreda BiH d.d - Sarajevo (EPBiH). Distribution activity in an open electricity market is not a market activity, but the regulated activity, and although does not participate directly in the electricity market, its action must allow unhindered development and functioning of the market in which suppliers competes. The introduction of the smart grid concept in electricity distribution activity is a particularly challenging area of research due to the actuality and importance. The paper presents the elements of the business framework of EPBiH in terms of application of modern technologies in the area of smart grid and the changes that inevitably occur during transition process of energy sector in general

Analysis of power consumption presents a very important issue for power distribution system operators. Some power system processes such as planning, demand forecasting, development, etc.., require a complete understanding of behaviour of power consumption for observed area , which requires appropriate techniques for analysis of available data. In this paper, two different time-frequency techniques are applied for analysis of hourly values of active an d reactive power consumption from one real power distribution transformer substation in urban part of Sarajevo city . Using the continuous wavelet transform (CWT) with wavelet power spectrum and global wavelet spectrum some properties of analysed time series are determined. Then, empirical mode decomposition (EMD) and Hilbert -Huang Transform (HHT) are applied for the analyses of the same time series and the results show ed that both applied approaches can provide very useful information about the behaviour of power consumption for observed time interval and different period (frequency) bands. Also it can be noticed that the results obtained by global wavelet spectrum and marginal Hilbert spectrum are very similar, thus confirming that both approaches could be used for identification of main properties of active and reactive power consumption time series .

Electricity demand forecasting is one of the most important components in the power system analysis. Furthermore, it is difficult and complicated process to forecast energy consumption. This study deals with modeling of the electrical energy consumption in Bosnia and Herzegovina in order to forecast future consumption of electrical loads based on temperature variables using machine learning methods. We used three different  machine learning methods for analyzing short term forecasting. The methods were trained using historical load data, collected from JP Elektroprivreda electrical power utility in BiH, and also considering weather data which is known to have a big impact on the use of electric power. Comparing the results it was seen that prediction for 500 hours is pretty good in range from 92,92% for reactive power till 98.84% for active power. Four different parameters were analyzed mean absolute error, root mean squared error, relative absolute error and root relative square error. The best results for apparent power were gotten with linear regression and are presented as for mean absolute error 9.84, root mean squared error 13.62, relative absolute error 14.06%, root relative squared error 14.39%. It is also seen from the results that,  the short term power consumption can be predicted which is important for maintaining of the voltage at the consumer side.

In this paper, the relationship between the Gross Domestic Product (GDP), air temperature variations and power consumption is evaluated using the linear regression and Wavelet Coherence (WTC) approach on a 1971-2011 time series for the United Kingdom (UK). The results based on the linear regression approach indicate that some 66% variability of the UK electricity demand can be explained by the quarterly GDP variations, while only 11% of the quarterly changes of the UK electricity demand are caused by seasonal air temperature variations. WTC however, can detect the period of time when GDP and air temperature significantly correlate with electricity demand and the results of the wavelet correlation at different time scales indicate that a significant correlation is to be found on a long-term basis for GDP and on an annual basis for seasonal air-temperature variations. This approach provides an insight into the properties of the impact of the main factors on power consumption on the basis of which the power system development or operation planning and forecasting the power consumption can be improved.