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EVALUATION OF LOAD FORECAST MODEL PERFORMANCE IN CROATIAN DSO

Ivona Sičaja ; Končar – Power Plant and Electric Traction Engineering
Ante Previšić ; Končar – Power Plant and Electric Traction Engineering
Matija Zečević ; Končar – Power Plant and Electric Traction Engineering
Domagoj Budiša ; HEP ODS d.o.o. Elektroslavonija Osijekb Croatia

Sažetak

During the revitalization of the Remote Control Systems of four Distribution System
Operators in Croatia: Elektra Zagreb, Elektroslavonija Osijek, Elektroprimorje Rijeka and
Elektrodalmacija Split, the load forecasting subsystems were implemented as an integral part
of the DMS system.
Accurate electricity load forecasting presents an important challenge in managing
supply and demand of electricity since it cannot be stored and has to be consumed immediately.
Electricity consumption forecasting has an important role in the scheduling, capacity and
operational planning of the distribution power system. Load forecasting of certain parts or the
whole distribution network helps to improve distribution network planning, operation and
control which also increases the safety level of the entire distribution system.
Although many forecasting methods were developed, none can be generalized for all
load patterns. Accurate results of electricity load models are essential to make important
decisions in planning and controlling so it is important to keep models as accurate as possible
regarding input variables such as historical loads and meteorological data. This article gives a
description of the implemented load forecasting subsystems using an artificial neural network
with a feedforward multilayer perceptron and backpropagation as a learning strategy. The
emphasis is on the simple and systematic use of input and output data as well as on forecasting
scenarios of specific measured points where hourly forecasted results for a week ahead are
presented and compared for Croatian Distribution Centers.

Ključne riječi

Short-Term Load Forecasting; Artificial Neural Networks; Load Forecast Model; Parameters Tuning

Hrčak ID:

213487

URI

https://hrcak.srce.hr/213487

Datum izdavanja:

1.8.2018.

Posjeta: 656 *