1. Miloš Simonović, Univerzitet u Nišu, Mašinski fakultet, Serbia
2. Vlastimir Nikolic, Univerzitet u Nišu, Mašinski fakultet, Serbia
3. Ivan Ćirić, Univerzitet u Nišu, Mašinski fakultet, Serbia
4. Emina Petrovic, Univerzitet u Nišu, Mašinski fakultet, Serbia
District heating companies have growing and significant need for improving economic and energy efficiency. Also, they have a challenge to keep the cost of produced and delivered heating energy as lower as possible. That is why it is very important to optimize production of heating energy using better prediction and control of customer needs. In this paper, the focus is on short-term prediction. Real historical data are used from city of Nis, southeastern Serbia, heating plant Krivi vir, 128 MW installed power. This prediction is particularly important for heating in transient regimes which unlike the standard heating regime does not have continuous supply of heating energy throughout the specified heating time period. An application of neural networks is realized based on original historical data of heating source by using recurrent neural network to fulfill demands on variation in ambient temperature during a heating day and satisfied results are obtained.
Ključne reči :
Tematska oblast:
Energy and Thermal Engineering
Datum:
16.03.2015.
