Forecasting Turkey's Short Term Hourly Load with Artificial Neural Networks

2010-04-22
BİLGİÇ KÜÇÜKGÜVEN, MERİÇ
Girep, C. P.
ASLANOĞLU, SAİME YEŞER
AYDINALP KÖKSAL, MERİH
Load forecasting is important necessity to provide economic, reliable, high grade energy. In this study, short term hourly load forecasting systems were developed for nine load distribution regions of Turkey using artificial neural networks (ANN) approach. ANN is the most commonly preferred approach for load forecasting. The mean average percent error (MAPE) of total hourly load forecast for Turkey is found as 1.81%.

Suggestions

Short term load forecasting using genetically optimized neural network cascaded with a Modified Kohonen clustering process
Erkmen, İsmet; Ozdogan, A (1997-07-18)
In this study, a new intelligent approach is developed for short term load forecasting (STLF). The technique consists of three basic modules. The first module employ the clustering of daily load curves using Modified Kohonen algorithm (MKA). Second module determine the most appropriate supervised neural network topology and associated initial weight values for each cluster, extracted from the historical data base, by using genetic algorithm (GA). At the third module, genetically optimized three layered back...
ARIMA forecasting of primary energy demand by fuel in Turkey
Ediger, Volkan S.; Akar, Sertac (2007-03-01)
Forecasting of energy demand in emerging markets is one of the most important policy tools used by the decision makers all over the world. In Turkey, most of the early studies used include various forms of econometric modeling. However, since the estimated economic and demographic parameters usually deviate from the realizations, time-series forecasting appears to give better results. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) methods to estimate ...
Assessment of solar data estimation models for four cities in Iran
Jahani, Elham; Sadati, S. M. Sajed; Yousefzadeh, Moslem (2015-04-29)
The estimated solar resources are important for designing renewable energy systems since measured data are not always available. The estimation models have been introduced in several studies. These models are mainly dependent on local meteorological data and need to be assessed for different locations and times. The current study compares the results of Angstrom's model and a neural network (NN) model developed for this study with measured data for four cities in Iran. The time resolution for the estimated ...
Solar Power Generation Analysis and Forecasting Real-World Data Using LSTM and Autoregressive CNN
tosun, nail; sert, egemen; Ayaz, Enes; YILMAZ, ekin; GÖL, MURAT (2020-09-22)
Generated power of a solar panel is volatile and susceptible to environmental conditions. In this study, we have analyzed variables affecting the generated power of a 17.5 kW real-world solar power plant with respect to five independent variables over the generated power: irradiance, time of measurement, panel's temperature, ambient temperature and cloudiness of the weather at the time of measurement. After our analysis, we have trained three different models to predict intra-day solar power forecasts of th...
Forecasting Turkey's sectoral energy demand
Oğuz, Mustafa Efe; Ekici, Tufan; Sarı, Ramazan; Sustainable Environment and Energy Systems (2013-6)
This study forecasts the sectoral energy demand of Turkey in the agriculture, industry, transportation, residence and services sectors for 2023 by means of the ARIMA, Vector Autoregressive and Decomposition statistical methods, and their products are then combined to arrived at a composite, or ensemble forecast. Each of these methods has their own merits and compliments each other. Two scenarios are considered; either the use of entire, unedited data (scenario one), or the absence of the last 3 years of the...
Citation Formats
M. BİLGİÇ KÜÇÜKGÜVEN, C. P. Girep, S. Y. ASLANOĞLU, and M. AYDINALP KÖKSAL, “Forecasting Turkey’s Short Term Hourly Load with Artificial Neural Networks,” 2010, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67383.