LEE- Elektrik Mühendisliği-Yüksek Lisans
Bu koleksiyon için kalıcı URI
Gözat
Konu "deep learning" ile LEE- Elektrik Mühendisliği-Yüksek Lisans'a göz atma
Sayfa başına sonuç
Sıralama Seçenekleri
-
ÖgeDeep learning for wind energy systems using the hurst exponent and statistical parameters(Graduate School, 2021-08-14) Alafi, Behnaz ; Şeker, Şahin Serhat ; 504181008 ; Electrical Engineering ; Elektrik MühendisliğiAs we all know, energy demand is continuously increasing because of population growth and developing technology. As a result of this increasing demand, energy shortages and environmental pollution will occur. Besides, because of the growing crisis and other critical issues around energy, renewable energy is taking countries' attention and becoming important in various parts of the entire world. Wind energy, solar power, tidal energy, geothermal energy, etc. as renewable energy sources have been used to solve these issues. Among these alternative sources of energy, wind and solar energy have got the most attention recently. Since wind power has less pollution, shorter construction time, less occupation, and flexible investment, it has become one of the most effective sources of energy. And in this study, the information is about wind data. But the wind is unstable and mainly affected by meteorological and navigational conditions and the principle for its implementation changes from one place to another. These changes in the meteorological measurement cause uncertainty in wind farms' generated power that affects power supply and quality. Also, because it is impossible to generate every power amount by wind energy or store electrical energy, there is a limitation on the amount of output power. Therefore, An accurate prediction can cause the cost of power generation reduction, less winding reserve capacity of the grid, and more reliable operation of the grid. Because of aforesaid reasons, prediction in wind energy systems is a very important issue. Nowadays, deep neural networks have been considering for prediction problems. In this study, the convolutional neural network(CNN) as a deep neural network is used to do predictions in wind energy systems based on meteorological data of one station. Since the Hurst exponent H is used to determine the predictability degree of a set of data, it gives some information about data that is useful in developing predictive models both theoretical and computational in nature. We first aim to apply the Hurst exponent method on wind energy data and then execute a deep neural network on data to tarin data through that deep neural network. Work steps: this literature study on the yearly meteorological features of one station applies deep learning methods to it. First of all, we gathered reported data for wind speed, air pressure, and relative humidity as the inputs of one deep neural network to train that network for predicting wind speed data. Since the power of one turbine is related to wind speed value, studying the wind speed behavior of one location leads to the study of the power capacity of that location. Before training a neural network, it is better to study the behavior of wind speed and find its statistical model and predictability degree, so before entering meteorological data into a deep neural network we studied statistical parameters of wind speed and find the probability density of it and then we found Hurst exponent, as the factor for predictability degree, and, then, all data is entered to one CNN to tarin that network and predict wind speed data.