Prediction of Wind Speed and Power in the Central Anatolian Region of Turkey by Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

نویسندگان

  • Ertuğrul ÇAM
  • Osman YILDIZ
چکیده

An adaptive neuro-fuzzy inference systems (ANFIS) model was used for predicting regional average wind speed and power values in the Central Anatolian region of Turkey. In model development, longitude, latitude and altitude of wind stations and wind speed measurement height were taken as input variables, while wind speed and power values were taken as output variables for 4 different surface roughness characteristics. After a successful learning and training process the proposed model produced reasonable mean errors ranging from 0.19% to 2.89% and negligible root mean square errors in training and testing wind speed and wind power data. Overall, the study results suggest that the ANFIS model can be used as an effective tool to estimate average wind speed and power values in the study area.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Adaptive Network-based Fuzzy Inference System-Genetic Algorithm Models for Prediction Groundwater Quality Indices: a GIS-based Analysis

The prediction of groundwater quality is very important for the management of water resources and environmental activities. The present study has integrated a number of methods such as Geographic Information Systems (GIS) and Artificial Intelligence (AI) methodologies to predict groundwater quality in Kerman plain (including HCO3-, concentrations and Electrical Conductivity (EC) of groundwater)...

متن کامل

Coastal Water Level Prediction Model Using Adaptive Neuro-fuzzy Inference System

This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, U. S. A., were obtained. A portion of the data was used to train the ANFIS network, wh...

متن کامل

Comparison of autoregressive integrated moving average (ARIMA) model and adaptive neuro-fuzzy inference system (ANFIS) model

Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...

متن کامل

Prediction of toxicity of aliphatic carboxylic acids using adaptive neuro-fuzzy inference system

Toxicity of 38 aliphatic carboxylic acids was studied using non-linear quantitative structure-toxicityrelationship (QSTR) models. The adaptive neuro-fuzzy inference system (ANFIS) was used to construct thenonlinear QSTR models in all stages of study. Two ANFIS models were developed based upon differentsubsets of descriptors. The first one used log ow K and LUMO E as inputs and had good predicti...

متن کامل

Modelling the formation of Ozone in the air by using Adaptive Neuro-Fuzzy Inference System (ANFIS) (Case study: city of Yazd, Iran)

The impact of air pollution and environmental issues on public health is one of the main topics studied in manycities around the world. Ozone is a greenhouse gas that contributes to global climate. This study was conducted topredict and model ozone of Yazd in the lower atmosphere by an adaptive neuro-fuzzy inference system (ANFIS). Allthe data were extracted from 721 samples collected daily ove...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006