Abstract—The demand characteristic is the most important
one in analyzing customer information. In a distribution
network, there is in any moment certain degree of uncertainty
about busses loads, and consequently, about load level of
network, busses voltage level, and power losses. Therefore, it is
very important to estimate first of all the load profiles of buses,
using available data (measurements effectuated in distribution
stations). The results obtained for various distribution stations
demonstrate the effectiveness of the present method in
overcoming the difficulties encountered in optimal planning
and operation of distribution networks.
Index Terms—load profile, clustering techniques, data flow
analysis, power consumption, distribution station
Electric distribution networks have a large number of load
busses, even if we were to take into consideration only the
busses with substations. The consumers connected in the
network busses are also very numerous, heterogeneous as
the absorbed powers, using their technologists, social
behaviors, enforcing the particular loop of thing.
In addition, the multiple participants in the electricity
market need new business strategies for providing value
added services to customer. They need, therefore, accurate
customer information about the electricity demand. The
Demand characteristic is the most important one for
analyzing customer information.
These difficulties are eliminated if for the distribution
networks analysis a daily load curve is used for each bus,
within characteristic regimes (winter and summer, working
day and weekend day).
The models for electric loads determinations are different,
depending on the networking tips: urban, rural or industrial.
Variation in time of the electric load reflects the graphic of
daily, seasonal and annual load, which indicate the real
electric energy consumption.
In this paper, load profile data, which can be collected by
means of the automatic meter reading system, are analyzed
in order to get demand patterns of customers. The load
profile data include electricity demand at a 15 minutes
interval. An algorithm for clustering similar patterns is
developed using the load profile data. As a result of the
classification, representative curves for the same groups are
generated. The demand characteristics of the groups are
II. GENERAL CONSIDERATIONS
Since the storage of electric energy on a large scale is not
possible, the main role of the power network is to transport
the demanded energy to consumers. Therefore, it is very
important to study and analyze the evolution of the load in
order to operate and design the power network. All the other
decisions are based on the consumed energy such as the load
forecast, the voltage control, determination of the peak load
for various types of consumer, calculations of the power
losses or power losses estimations, proper tariff design, etc.
Figure 1. Load profile for different seasons.
The main causes generating load modifications are:
• weather conditions: the season, the daily
temperatures, the speed of the wind, etc; (Fig. 1)
• demographic factors: the growth rate of the
population, the number of the inhabitants in a
certain area, the birth rate, etc;
• economic factors: the gross national product, the
labor productivity, the economy development
rate, the level of life quality and a very important
element: the price of energy.
The evolution in time of these parameters has a strong
random character. At a certain moment, the more or less
accidental realization of these parameters directly influences
the load and its variation change tendency influences in a
decisive way the load curves.