Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements

García, Irene García; Huo, Stella; Prado, Raquel; Bravo, Lelys

doi:10.1016/j.renene.2020.05.182

Cited by 18 publications

(9 citation statements)

References 13 publications

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“…Matrix-varied models have been used to facilitate the estimation of the covariance matrix of m time series. Modeling of wind intensity and direction is a recent example (Garcia et al 50 ), in which case m = 2.…”

Section: Linear Matrix-variable Dynamic Modelsmentioning

confidence: 99%

A review of Bayesian dynamic forecasting models: Applications in marketing

Migon

Alves

Menezes

et al. 2023

Appl Stoch Models Bus & Ind

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We briefly review the main developments of Bayesian dynamic models. The emphasis is on marketing applications. Typical examples in this area are discussed. The concepts of monitoring and intervention are carefully explained with illustrative examples and open source computational routines. We avoid algebraic developments and instead use graphical examples to illustrate theoretical aspects. Two real‐world problems using Bayesian dynamic models are discussed. Finally, we describe recent developments and alternative proposals to formally address the dependence when dealing with the modeling of multiple time series.

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Section: Linear Matrix-variable Dynamic Modelsmentioning

confidence: 99%

A review of Bayesian dynamic forecasting models: Applications in marketing

Migon

Alves

Menezes

et al. 2023

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

show abstract

“…These methods use available time series of historical wind speed and/or power data to make predictions. For example, some of them approach the forecasting problem while using conventional statistical methods, including Autoregressive Integrated Moving Average (ARIMA) models [ 13 , 14 , 15 , 16 , 17 , 18 ], Bayesian regression [ 19 , 20 , 21 ], or Kalman filtering [ 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…Because of the high latency and computational cost of NWP, physical methods have limited utility for short-term predictions, although they can perform well for long forecasting horizons (greater than 6 h). Different from them, statistical methods model wind speed/power as a stochastic process formed from the available time series of historical data [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. These models have lower complexity and latency than the physical ones, so they are preferred for short-term forecasting horizons.…”

Section: Introductionmentioning

confidence: 99%

Efficient Wind Speed Forecasting for Resource-Constrained Sensor Devices

Herrería-Alonso

Suárez-González

Rodríguez-Pérez

et al. 2021

Sensors

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Wind energy harvesting technology is one of the most popular power sources for wireless sensor networks. However, given its irregular nature, wind energy availability experiences significant variations and, therefore, wind-powered devices need reliable forecasting models to effectively adjust their energy consumption to the dynamics of energy harvesting. On the other hand, resource-constrained devices with limited hardware capacities (such as sensor nodes) must resort to forecasting schemes of low complexity for their predictions in order to avoid squandering their scarce power and computing capabilities. In this paper, we present a new efficient ARIMA-based forecasting model for predicting wind speed at short-term horizons. The performance results obtained using real data sets show that the proposed ARIMA model can be an excellent choice for wind-powered sensor nodes due to its potential for achieving accurate enough predictions with very low computational burden and memory overhead. In addition, it is very simple to setup, since it can dynamically adapt to varying wind conditions and locations without requiring any particular reconfiguration or previous data training phase for each different scenario.

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“…These models directly approach wind as a time series data and they forecast wind speed by determining the underlying statistical patterns and using autocorrelation functions (Liu et al, 2020a). The most common statistically approached predictions are made with autoregression (Poggi et al, 2003), autoregressive moving average (Erdem and Shi, 2011), autoregressive integrated moving average (ARIMA) (Cadenas and Rivera, 2007), Markov chain (Carpinone et al, 2015), and Bayesian model (Garcı´a et al, 2020). Since their biggest shortcoming is the inability to capture nonlinear patterns in the data, the long-term horizon accuracy of the forecasts made with these models is generally low.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive country-based day-ahead wind power generation forecast model by coupling numerical weather prediction data and CatBoost with feature selection methods for Turkey

Özen

Deniz

2022

Wind Engineering

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A country-based day-ahead wind power generation forecast (WPGF) model with a grid selection algorithm and feature selection models was proposed in this study. Atmospheric variables extracted from 300, 500, 700 hPa pressure levels, and surface level of ERA5 reanalysis data with 2.5° spatial resolution were used to train/validate the categorical boosting (CatBoost) model. A special grid selection algorithm was proposed by considering Turkey’s spatial distribution of wind power plants. The day-ahead forecasts of ECMWF’s HRES (High-resolution) were used as the test subset, therefore, paving the way for the operational use of the model. The proposed model could be considered much as a specialized machine learning based downscaling method for country-based WPGF due to using numerical weather prediction model outputs as its input. Results showed that the proposed model that uses fewer features has outperformed the other models with a normalized root mean square error of 7.6% and coefficient of determination of 0.8989.

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Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements

Cited by 18 publications

References 13 publications

A review of Bayesian dynamic forecasting models: Applications in marketing

A review of Bayesian dynamic forecasting models: Applications in marketing

Efficient Wind Speed Forecasting for Resource-Constrained Sensor Devices

A comprehensive country-based day-ahead wind power generation forecast model by coupling numerical weather prediction data and CatBoost with feature selection methods for Turkey

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