scholarly journals Multiperiod-Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Chen ◽  
Zhijun Li ◽  
Yi Zhang
Keyword(s):  
Wind Speed ◽  
Ensemble Learning ◽  
Analysis Of Variance ◽  
Large Scale ◽  
Fuzzy Inference ◽  
Feature Learning ◽  
Denoising Autoencoder ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Learning Machine

Accurate forecasting of wind speed plays a fundamental role in enabling reliable operation and planning for large-scale integration of wind turbines. It is difficult to obtain the accurate wind speed forecasting (WSF) due to the intermittent and random nature of wind energy. In this paper, a multiperiod-ahead WSF model based on the analysis of variance, stacked denoising autoencoder (SDAE), and ensemble learning is proposed. The analysis of variance classifies the training samples into different categories. The stacked denoising autoencoder as a deep learning architecture is later built for unsupervised feature learning in each category. The ensemble of extreme learning machine (ELM) is applied to fine-tune the SDAE for multiperiod-ahead wind speed forecasting. Experimental results are made to demonstrate that the proposed model has the best performance compared with the classic WSF methods including the single SDAE-ELM, ELMAN, and adaptive neuron-fuzzy inference system (ANFIS).

scholarly journals Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1958 ◽  
Author(s):  
Lilin Cheng ◽  
Haixiang Zang ◽  
Tao Ding ◽  
Rong Sun ◽  
Miaomiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Fuzzy Inference ◽  
Ensemble Model ◽  
Probabilistic Forecasting ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Power Integration ◽  
Neuro Fuzzy

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as submodels for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deeplearningbased submodels. Lastly, variances are obtained from submodels and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

A novel purification machine and fuzzy inference method based hybrid model for wind speed forecasting

2020 ◽  
Vol 39 (3) ◽  
pp. 4059-4070
Author(s):  
Weina Ren ◽  
Chengdong Li ◽  
Peng Wen
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Hybrid Model ◽  
Fuzzy Inference ◽  
Renewable Energy Sources ◽  
Inference Method ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Speed Data ◽  
Negative Effect

As one kind of readily available renewable energy sources, wind is widely used in power generation where wind speed plays an important role. Generally speaking, we need to forecast the wind speed for improving the controllability of wind power generation. However, there exists considerable randomness and instabilities in wind speed data so that it is difficult to obtain accurate forecasting results. In this paper, we propose a novel fuzzy inference method based hybrid model for accurate wind speed forecasting. In this hybrid model, we adopt two strategies to enhance the estimation performance. On one hand, we propose the purification machine which utilize the Irregular Information Reduction Module (IIRM) and the Irrelevant Variable Reduction Module (IVRM) to reduce the randomness and instabilities of the data and to eliminate the variables with zero or negative effect in the wind speed time series. On the other hand, we adopt the developed Single-Input-Rule-Modules based Fuzzy Inference System (SIRM-FIS), the functionally weighted SIRM-FIS (FWSIRM-FIS) to realize the prediction of wind speed. This FWSIRM-FIS utilizes the multi-variable functional weights to dynamically measure the importance of the input variables so that the input-output mapping can be strengthened and more accurate forecasting results can be achieved. Furthermore, detailed experiments and comparisons are given. Experimental results demonstrate that the proposed FWSIRM-FIS and purification machine contributes greatly to deal with the randomness and instability in the wind speed data and yield more accurate forecasting results than those existing excellent forecasting models.

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

2020 ◽  
Vol 9 (5) ◽  
pp. 389-391
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Unit Commitment ◽  
Fuzzy Inference ◽  
Time Interval ◽  
Power Prediction ◽  
Short Term ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Neuro Fuzzy

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.

scholarly journals Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 793
Author(s):  
Abdul Razzaq Ghumman ◽  
Mohammed Jamaan ◽  
Afaq Ahmad ◽  
Md. Shafiquzzaman ◽  
Husnain Haider ◽  
...  
Keyword(s):  
Wind Speed ◽  
Water Resources ◽  
Arid Regions ◽  
Fuzzy Inference ◽  
Sunshine Duration ◽  
Pan Evaporation ◽  
Estimation Of Parameters ◽  
Inference System ◽  
Daunting Task ◽  
The Impact

The evaporation losses are very high in warm-arid regions and their accurate evaluation is vital for the sustainable management of water resources. The assessment of such losses involves extremely difficult and original tasks because of the scarcity of data in countries with an arid climate. The main objective of this paper is to develop models for the simulation of pan-evaporation with the help of Penman and Hamon’s equations, Artificial Neural Networks (ANNs), and the Artificial Neuro Fuzzy Inference System (ANFIS). The results from five types of ANN models with different training functions were compared to find the best possible training function. The impact of using various input variables was investigated as an original contribution of this research. The average temperature and mean wind speed were found to be the most influential parameters. The estimation of parameters for Penman and Hamon’s equations was quite a daunting task. These parameters were estimated using a state of the art optimization algorithm, namely General Reduced Gradient Technique. The results of the Penman and Hamon’s equations, ANN, and ANFIS were compared. Thirty-eight years (from 1980 to 2018) of manually recorded pan-evaporation data regarding mean daily values of a month, including the relative humidity, wind speed, sunshine duration, and temperature, were collected from three gauging stations situated in Al Qassim, Saudi Arabia. The Nash and Sutcliffe Efficiency (NSE) and Mean Square Error (MSE) evaluated the performance of pan-evaporation modeling techniques. The study shows that the ANFIS simulation results were better than those of ANN and Penman and Hamon’s equations. The findings of the present research will help managers, engineers, and decision makers to sustainability manage natural water resources in warm-arid regions.

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