A wind speed forecasting model based on artificial neural network and meteorological data

Finamore, Antonella R.; Calderaro, Vito; Galdi, Vincenzo; Piccolo, Antonio; Conio, Gaspare

doi:10.1109/eeeic.2016.7555671

Cited by 13 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This high accuracy concerns very low speeds, mostly 2 to 4 m/s. After training with meteorological data, an MLP using real data achieved an MSE of 5.27 to 7.05 m 2 /s 2 for a 24-hr forecast [6].…”

Section: Previous Workmentioning

confidence: 99%

Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions

et al. 2021

View full text Add to dashboard Cite

We focus on deep learning algorithms, improving upon the Weather Research and Forecasting (WRF) model, and we show that the combination of these methods produces day-ahead wind speed predictions of high accuracy, with no need for previous-day measurements. We also show that previous-day data, if available, offer a significant enhancement. Our main contribution is the design and testing of original neural networks that capture both spatial and temporal characteristics of the wind, by combining convolutional (CNN) as well as recurrent (RNN) neural networks. The input predictions are obtained by a WRF model that we appropriately parameterize; we also specify a grid adapted to each park so as to capture its topography. Training uses historical data from 5 wind farms in Greece, and the 5-month testing period includes winter months, which exhibit the highest wind speed values. Our models improve WRF accuracy on average by 19.4%, and the improvement occurs in every month; expectedly, the improvement is lowest for the park where WRF performs best. Our neural network is competitive to state-of-the-art models, achieving an average MAE of 1.75 m/s. Accuracy improves for speed values up to 20 m/s, which are important in wind energy prediction. We also develop

show abstract

Section: Previous Workmentioning

confidence: 99%

Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The weight (w) and threshold (b) of each node are processed on the mathematical equation given in Eq. (2) until the target output value is reached [1,3,4,13,16].…”

Section: Statistical Approachesmentioning

confidence: 99%

“…There is a need for computers called supercomputers in order to operate properly of NWF. So it cannot be used anywhere and cannot be measured [3,7,9,10,11].…”

Section: Physical Approachesmentioning

confidence: 99%

“…So, if it is aimed to achieve healthy and reliable measurement results, the data of expert institutions in the field should be utilized. In these measurements, the main goal is to determine the wind speed characteristic of the relevant zone and to calculate the wind power [3,5]. The factor directly affecting production in wind-driven power plants is wind power.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determination of Wind Potential of a Specific Region using Artificial Neural Networks

Taşdemir

Yanıktepe

Guher

2017

ijisae

View full text Add to dashboard Cite

Abstract:There is a widespread trend in alternative energy sources in today's world. Achieving energy without harming the environment has been the most important target of the countries in recent years. For this reason, it is necessary to make utmost use of natural energy sources such as wind, sun and water. Among these sources, wind energy is the most utilized. Because it was cheap and quickly return to investment it is carried out many studies in this area. However, the most important problem is the continuity when the wind energy is obtained. The first thing to do before a wind power plant is installed in a region is to calculate the wind potential of the area concerned. This process is long-term under normal conditions. Artificial Neural Networks (ANN) is one of the most frequently used methods for determining a wind power potential in a short time period. In this study, it is aimed to estimate the wind potential of a certain region within the boundaries of Osmaniye province. ANN was used to estimate the wind power potential. As a result of comparing the statistical values of the forecast values with the measured actual values, the performance of the method applied is indicated. The meteorology station at Osmaniye Korkut Ata University using data has been successfully estimated wind potential.

show abstract

“…Then, evaluating the errors (formula 4) in the calibration dataset, the ANN technique can be implemented to forecast the random component. The neural network adopted by the authors is a feed forward backpropagation network (see for instance [40][41][42][43][44][45]). In particular, two ANN have been trained: the first, ANN(t-1), takes in input the errors of 7 periods prior the one that is going to be predicted; the second, ANN(t-7), takes in input the errors of 7 periods, starting from 14 periods prior the one that is going to be predicted.…”

Section: Models Presentationmentioning

confidence: 99%

A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

Guarnaccia¹,

Quartieri²,

Tepedino³

2017

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to "learn" the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN on the residuals. This hybrid model will exploit interesting features and results, with a significant variation related to the number of steps forward in the prediction. It will be shown that the best results, in terms of prediction, are achieved predicting one step ahead in the future. Anyway, a 7 days prediction can be performed, with a slightly greater error, but offering a larger range of prediction, with respect to the single day ahead predictive model.

show abstract

A wind speed forecasting model based on artificial neural network and meteorological data

Cited by 13 publications

References 8 publications

Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions

Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions

Determination of Wind Potential of a Specific Region using Artificial Neural Networks

A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

Contact Info

Product

Resources

About