Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

Jiang, Yu; Chen, Xingying; Yu, Kun; Liao, Yingchen

doi:10.1007/s40565-015-0171-6

Cited by 63 publications

(36 citation statements)

References 21 publications

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“…Based on the ACF and PACF plots we test a range of p and q parameters using the Bayesian information parameters. The literature review [13,21] shows that in many applications the model parameters are often between 1 to 2 for p and q and between 0 and 2 for d. In this paper, the optimal p, d, q are those which give the smallest BIC value. The BIC matrix calculations show that the best ARIMAX model order are equal to (1, 0, 2).…”

Section: Arimax and Arxmentioning

confidence: 98%

“…ARIMAX and ANN forecasting concepts have been applied widely in different energy applications such as buildings, industrial loads and renewable energy [13,14]. It should also be beneficial to apply these techniques to forecasting the RTG crane demand in order to improve the understanding of load behaviour which can help to reduce peak demand and gas emissions.…”

Section: Introductionmentioning

confidence: 99%

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Day-ahead industrial load forecasting for electric RTG cranes

Alasali

Haben

Becerra

et al. 2018

J. Mod. Power Syst. Clean Energy

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Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports. The move towards electrified rubber-tyred gantry (RTG) cranes is expected to reduce gas emissions and increase energy savings compared to diesel RTG cranes but it will increase electrical energy demand. Electrical load forecasting is a key tool for understanding the energy demand which is usually applied to data with strong regularities and seasonal patterns. However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge. This paper is one of the first extensive investigations into short term load forecasts for electrified RTG crane demand. Options for model inputs are investigated depending on extensive data and correlation analysis. The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well. The models are tested on two different RTG crane data sets that were collected from the Port of Felixstowe in the UK. The results reveal the effectiveness of the forecast models when the estimation of the number of crane moves and container gross weight are accurate.

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Section: Arimax and Arxmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Day-ahead industrial load forecasting for electric RTG cranes

Alasali

Haben

Becerra

et al. 2018

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

show abstract

“…Liu et al [18] propose a novel time-series technique that is based on the Taylor Kriging model. Other works combine multiple numerical techniques to produce ensemble wind forecasts [19][20][21]. Wang and Xiong [22] develop a hybrid forecasting method based on an ARMA process, outlier detection, and fuzzy time series to forecast the daily wind speed in Taiwan.…”

Section: Introductionmentioning

confidence: 99%

“…Wang and Xiong [22] develop a hybrid forecasting method based on an ARMA process, outlier detection, and fuzzy time series to forecast the daily wind speed in Taiwan. Jiang et al [21] propose a hybrid approach that employs a Boosting algorithm to improve the forecasting performance of a traditional ARMA model. They demonstrate the effectiveness of this technique using wind-production data from the east coast of Jiangsu Province, China.…”

Section: Introductionmentioning

confidence: 99%

A vector autoregression weather model for electricity supply and demand modeling

Liu

Roberts

Sioshansi

2018

J. Mod. Power Syst. Clean Energy

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Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large vector autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with 16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scores that are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between 6% and 80% in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation, temperature, and wind speed.

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“…However, the paper did not address neither implementation nor simulation details. The ARMAbased-enhanced boosting technique is mentioned for dayahead wind power prediction in a wind farm at Jiangsu province, China, in [11]. The method showed improvement in 15.5% MAE with respect to the ARMA model and 3.21% MAE for the persistence model.…”

Section: Introductionmentioning

confidence: 99%

Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval

Rafique

Zhang

Rafique

et al. 2018

International Journal of Photoenergy

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The accuracy of energy management system for renewable microgrid, either grid-connected or isolated, is heavily dependent on the forecasting precision such as wind, solar, and load. In this paper, an improved fuzzy prediction horizon forecasting method is developed to address the issue of intermittence and uncertainty problem related to renewable generation and load forecast. In the first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established coverage grade indicator to check the accuracy of interval forecast. Secondly, a wind, solar, and load forecaster is developed for renewable microgrid test bed which is located in Beijing, China. One day and one step ahead results for the proposed forecaster are expressed with lowest RMSE and training time. In order to check the efficiency of the proposed method, a comparison is carried out with the existing models. The fuzzy interval-based model for the microgrid test bed will help to formulate the energy management problem with more accuracy and robustness.

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Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

Cited by 63 publications

References 21 publications

Day-ahead industrial load forecasting for electric RTG cranes

Day-ahead industrial load forecasting for electric RTG cranes

A vector autoregression weather model for electricity supply and demand modeling

Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval

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