Wind Power Forecasting Based on Time Series and Neural Network
نویسندگان
چکیده
The wind farm output power have the characteristics of dynamic, random, large capacity etc, which brought great difficulty for incorporating the wind farm in the bulk power system. In order to rationally regulate the power supply system in large grid connected wind power system and reduce the spinning reserve capacity of the power supply system and operating costs, it is necessary to forecasting the capacity of wind power. For the randomness of the wind farm output, we use the ARMA (q, p) model of time series to forecast wind speed and atmospheric pressure, and using the RBF neural network based on this to forecast wind power. Taking the data of measured wind speed and atmospheric pressure from a wind farm as example, to validate the method described above, and the result show that the method has a certain practicality.
منابع مشابه
Optimal Modeling and Forecasting of Equipment Failure Rate for the Electricity Distribution Network
In order to gain a deep understanding of planned maintenance, check the weaknesses of distribution network and detect unusual events, the network outage should be traced and monitored. On the other hand, the most important task of electric power distribution companies is to supply reliable and stable electricity with the minimum outage and standard voltage. This research intends to use time ser...
متن کاملHourly Wind Speed Prediction using ARMA Model and Artificial Neural Networks
In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in I...
متن کاملComparison of Neural Network Models, Vector Auto Regression (VAR), Bayesian Vector-Autoregressive (BVAR), Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in Iran
This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...
متن کاملShort and Mid-Term Wind Power Plants Forecasting With ANN
In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...
متن کاملComparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison betw...
متن کاملCombination of Transformed-means Clustering and Neural Networks for Short-Term Solar Radiation Forecasting
In order to provide an efficient conversion and utilization of solar power, solar radiation datashould be measured continuously and accurately over the long-term period. However, the measurement ofsolar radiation is not available to all countries in the world due to some technical and fiscal limitations. Hence,several studies were proposed in the literature to find mathematical and physical mod...
متن کامل