Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation

Zhao, En-Guang; Zhao, Jing; Liu, Liwei; Su, Zhongyue; An, Ning

doi:10.3390/en9010007

Cited by 19 publications

(11 citation statements)

References 34 publications

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“…Table 2. Estimated coefficients entering Equations (12a)-(12c) of the ARIMA processes describing the stochastic evolution of the coefficients in Equations (11) and (13). Notice that each coefficient is modelled with an ARIMA process of different order: ARIMA(3, 1, 1) for θ 1 , ARIMA(1, 1, 0) for θ 2 , and ARIMA(2, 1, 2) for ζ.…”

Section: Nested Arima Model For Wind Speedsmentioning

confidence: 99%

“…Though using considerably more data, neural networks seem to guarantee improvements not better than 6%. ARIMA models with exogenous variables have been also developed [11] for modelling 15 min average wind speed. By studying wind speed series from China, it has been found that an ARIMA model combined with empirical mode decomposition has a better accuracy in comparison with neural networks and other machine learning approaches [12], achieving a mean absolute error smaller than 4%.…”

Section: Introductionmentioning

confidence: 99%

“…In (c,d), one shows similar plots for θ 2 . In (e,f) the time series and histogram of the noise strength ζ are shown (see Equation(11)).…”

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confidence: 99%

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Wind Speed Modeling by Nested ARIMA Processes

2018

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Wind speed modelling is of increasing interest, both for basic research and for applications, as, e.g., for wind turbine development and strategies to construct large wind power plants. Generally, such modelling is hampered by the non-stationary features of wind speed data that, to a large extent, reflect the turbulent dynamics in the atmosphere. We study how these features can be captured by nested ARIMA models. In this approach, wind speed fluctuations in given time windows are modelled by one stochastic process, and the parameter variation between successive windows by another one. For deriving the wind speed model, we use 20 months of data collected at the FINO1 platform at the North Sea and use a variable transformation that best maps the wind speed onto a Gaussian random variable. We find that wind speed increments can be well reproduced for up to four standard deviations. The distributions of extreme variations, however, strongly deviate from the model predictions.

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Section: Nested Arima Model For Wind Speedsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wind Speed Modeling by Nested ARIMA Processes

2018

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“…With different developed forecasting approaches, Zhao, Zhao, Liu, Su and An [23] conducted a study to forecast wind speed using the self-adaptive auto-regressive integrated moving average chaotic particle swarm optimization (SA-ARIMA-CPSO) approach. This approach was developed by a SA auto-regressive integrated moving average, with an exogenous variables (ARIMAX) model, through the optimization of the CPSO algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

Sutthichaimethee

Kubaha

2018

Sustainability

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The Thailand Development Policy focuses on the simultaneous growth of the economy, society, and environment. Long-term goals have been set to improve economic and social well-being. At the same time, these aim to reduce the emission of CO 2 in the future, especially in the construction sector, which is deemed important in terms of national development and is a high generator of greenhouse gas. In order to achieve national sustainable development, policy formulation and planning is becoming necessary and requires a tool to undertake such a formulation. The tool is none other than the forecasting of CO 2 emissions in long-term energy consumption to produce a complete and accurate formulation. This research aims to study and forecast energy-related carbon dioxide emissions in Thailand's construction sector by applying a model incorporating the longand short-term auto-regressive (AR), integrated (I), moving average (MA) with exogenous variables (Xi) and the error correction mechanism (LS-ARIMAXi-ECM) model. This model is established and attempts to fill the gaps left by the old models. In fact, the model is constructed based on factors that are causal and influential for changes in CO 2 emissions. Both independent variables and dependent variables must be stationary at the same level. In addition, the LS-ARIMAXi-ECM model deploys a co-integration analysis and error correction mechanism (ECM) in its modeling. The study's findings reveal that the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model is a forecasting model with an appropriate time period (t − i), as justified by the Q-test statistic and is not a spurious model. Therefore, it is used to forecast CO 2 emissions for the next 20 years (2019 to 2038). From the study, the results show that CO 2 emissions in the construction sector will increase by 37.88% or 61.09 Mt CO 2 Eq. in 2038. Also, the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model has been evaluated regarding its performance, and it produces a mean absolute percentage error (MAPE) of 1.01% and root mean square error (RMSE) of 0.93% as compared to the old models. Overall, the results indicate that determining future national sustainable development policies requires an appropriate forecasting model, which is built upon causal and contextual factors according to relevant sectors, to serve as an important tool for future sustainable planning.

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“…There has been a lot of work testing and improving various statistical correction methods in order to improve the forecast skill of NWPs [1]. Typical approaches include comparison and combination of different statistical models [11][12][13][14] and NWP datasets [15], and incorporating more input parameters.…”

Section: Introductionmentioning

confidence: 99%

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Chu

Wang

et al. 2016

Energies

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Abstract:Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28-52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

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Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation

Cited by 19 publications

References 34 publications

Wind Speed Modeling by Nested ARIMA Processes

Wind Speed Modeling by Nested ARIMA Processes

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

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