Short term wind power prediction using ANFIS

Kassa, Yordanos; Zhang, J. H.; Zheng, Dehua; Wei, Dan

doi:10.1109/icpre.2016.7871238

Cited by 34 publications

(27 citation statements)

References 11 publications

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“…For example, among statistical methods, the auto-regression integrated moving average (ARIMA) model is a time series analysis model [8], and various approaches have been suggested to overcome the disadvantage of ARIMA [9,10] which cannot accurately predict wind power due to the aforementioned error accumulation. The latter include neural network (NN) [11], fuzzy inference [12], particle swarm optimization (PSO) [13], genetic algorithm (GA) [14], support vector machine (SVM) [15], and long short-term memory (LSTM) [16]. The artificial intelligence method has superior performance for general purpose, but it has a disadvantage in that the relationship between model elements cannot be accurately explained.…”

Section: Of 17mentioning

confidence: 99%

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Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory

Son

Yang

2019

Energies

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Renewable energy has recently gained considerable attention. In particular, the interest in wind energy is rapidly growing globally. However, the characteristics of instability and volatility in wind energy systems also affect power systems significantly. To address these issues, many studies have been carried out to predict wind speed and power. Methods of predicting wind energy are divided into four categories: physical methods, statistical methods, artificial intelligence methods, and hybrid methods. In this study, we proposed a hybrid model using modified LSTM (Long short-term Memory) to predict short-term wind power. The data adopted by modified LSTM use the current observation data (wind power, wind direction, and wind speed) rather than previous data, which are prediction factors of wind power. The performance of modified LSTM was compared among four multivariate models, which are derived from combining the current observation data. Among multivariable models, the proposed hybrid method showed good performance in the initial stage with Model 1 (wind power) and excellent performance in the middle to late stages with Model 3 (wind power, wind speed) in the estimation of short-term wind power. The experiment results showed that the proposed model is more robust and accurate in forecasting short-term wind power than the other models.

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Section: Of 17mentioning

confidence: 99%

“…As shown Equation (12), its results are decided by the current input and memory and output of the previous hidden layer.…”

Section: Output Gate Layermentioning

confidence: 99%

Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory

Son

Yang

2019

Energies

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“…In other words,t he dramaticg rowtho fr enewable energy occurred in the last 15 years,i nE urope first and across the entire world later, has changed the scope of meteorology in the energy sector from at ool to improve the risk management of energyt oamore comprehensive objective of improvingt he resilience of todayse nergy systems in which water, wind, ands un play ac rucialr ole.N umerous meteorological tools are already available for such predictions,f rom the Anemos [19] or the Anfis [20] modelsu sed for wind-power forecastingi nE urope or in China to the Europeanc limatic energy mixes (ECEM) model to integrate weather forecasts with climate models,t oe nable assessment of how well different energy supply mixes will meet the demando fe lectricity in Europe. [21] What is essential to understand is that these tools provide datasetst hat allow the prediction of wind availability and solar radiation in different geographical and time domains, assistingt he regional (andn ational) planninga nd optimization of renewable and overall electricity generation and dispatchment.…”

Section: Weather and Energy:f Rom Demand To Supply Managementmentioning

confidence: 99%

New Energy and Weather Services in the Context of the Energy Transition

et al. 2017

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The potential of weather forecasting is increasingly being exploited by power generators and transmission‐system operators as renewable electricity generation has become a significant and increasing part of the energy mix in many countries. Showing how the forecasts are interpreted and actually used, we suggest avenues for policy makers on how best utilizing meteorology in the unfolding energy transition through new, integrated meteorology and energy services.

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“…Combination of ANN and Fuzzy logic approaches perform better than individual ANN and Fuzzy forecasts, and ANN-Fuzzy approach provides excellent performance. When added with ANN, FIS acts as a feedback and gains more experience and produces accurate output [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-fuzzy Inference System Based Short Term Wind Speed Forecasting

Vanitha*¹,

Mary²,

Jasmine³

et al. 2020

IJITEE

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Due to the stochastic nature of wind speed, accurate wind power prediction plays a major challenge to power system operators for unit commitment and load dispatching. To predict wind power production with great accuracy, wind speed forecasting in different time horizons is gaining importance nowadays. This paper explores the application of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to forecast the wind speed in Logan international airport, USA for one year in every one hour time interval. ANFIS with different structures and membership functions are trained to find out the best model to do short term wind forecasting. Simulation with the best model is performed in MATLAB and the results show that the three input model with wind speed, direction and air pressure as inputs using Gaussian bell membership function provides the smallest errors.

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Short term wind power prediction using ANFIS

Cited by 34 publications

References 11 publications

Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory

Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory

New Energy and Weather Services in the Context of the Energy Transition

Adaptive Neuro-fuzzy Inference System Based Short Term Wind Speed Forecasting

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