Four methods for short-term load forecasting using the benefits of artificial intelligence

Erkmen, İsmet
Topalli, AK
Four methods are developed for short-term load forecasting and are tested with the actual data from the Turkish Electrical Authority. The method giving the most successful forecasts is a hybrid neural network model which combines off-line and on-line learning and performs real-time forecasts 24-hours in advance. Loads from all day types are predicted with 1.7273% average error for working days, 1.7506% for Saturdays and 2.0605% for Sundays.


Altinoz, Okkes Tolga; KOŞALAY, İLHAN; Gezer, Derya (VSB - Technical University of Ostrava, 2020-06-01)
Speed governors have critical importance on hydroelectric power plants, which are adjusted to the rotating speed of hydroelectric generation based on load demand of the grid. The rotating speed is the main factor to balance power generation and load demand. The well-designed controller is needed to control speed governors with high accuracy. A well-defined model is needed to obtain desired control structure. Therefore, in this study, initially, the mathematical model of a hydroelectric power plant is obtain...
Investigation of radiative heat transfer in freeboard of a 0.3 MWt AFBC test rig
Kozan, M; Selçuk, Nevin (2000-01-01)
Based on the analysis of measured data on flow rates, concentrations and temperatures taken during steady state operation of a lignite-fired 0.3 MWt Atmospheric Fluidized Bed Combustor (AFBC) test rig, radiative exchange in freeboard of the combustor was modeled by using a well-stirred enclosure model in conjunction with Radiosity-Irradiation Method (RIM). Radiative properties of the particle laden combustion gases were calculated by assuming grey radiation behaviour for both particles and gas, and using Le...
Intelligent short-term load forecasting in Turkey
Topalli, Ayca Kumluca; Erkmen, İsmet; Topalli, Ihsan (Elsevier BV, 2006-09-01)
A method is proposed to forecast Turkey's total electric load one day in advance by neural networks. A hybrid learning scheme that combines off-line learning with real-time forecasting is developed to use the available data for adapting the weights and to further adjust these connections according to changing conditions. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for ...
The effects of hydro power plants’ governor settings on the turkish power system frequency
Cebeci, Mahmut Erkut; Ertaş, Arif; Department of Electrical and Electronics Engineering (2008)
This thesis proposes a method and develops a mathematical model for determining the effects of hydro power plants’ governor settings on the Turkish power system frequency. The Turkish power system suffers from frequency oscillations with 20 30 seconds period. Besides various negative effects on power plants and customers, these frequency oscillations are one of the most important obstacles before the interconnection of the Turkish power system with the UCTE (Union for the Coordination of Transmission of El...
Hybrid-shaped single-loop resonator: a four-band metamaterial structure
Yurduseven, O.; Yilmaz, A. E.; Sayan, Gönül (Institution of Engineering and Technology (IET), 2011-12-08)
The aim of this reported work is to demonstrate the feasibility of a miniaturised four-band metamaterial resonator which has potential use in the design of multiband microwave devices such as four-band mobile phone patch antennas. The suggested resonator topology is called the hybrid-shaped single-loop resonator (HSLR) as its unit cell is formed by the combination of square-shaped and triangular-shaped sections of a single piece metal loop printed on a planar low-loss dielectric substrate. The performance o...
Citation Formats
İ. Erkmen and A. Topalli, “Four methods for short-term load forecasting using the benefits of artificial intelligence,” ELECTRICAL ENGINEERING, pp. 229–233, 2003, Accessed: 00, 2020. [Online]. Available: