An Ensemble Forecasting Model of Wind Power Outputs Based on Improved Statistical Approaches

Kim, Yeojin; Hur, Jin

doi:10.3390/en13051071

Cited by 30 publications

(12 citation statements)

References 26 publications

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“…A third limitation of this study refers to the application of single forecasting models with our recommendations being based on selecting the best method on each occasion. An alternative would be the use of combinations, which has shown good performance in numerous studies [56], even in their simplest forms [57]. Here, we could consider combining the forecasts from all or selected pools of methods [58].…”

Section: Discussionmentioning

confidence: 99%

The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece

2020

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Weather variables are an important driver of power generation from renewable energy sources. However, accurately predicting such variables is a challenging task, which has a significant impact on the accuracy of the power generation forecasts. In this study, we explore the impact of imperfect weather forecasts on two classes of forecasting methods (statistical and machine learning) for the case of wind power generation. We perform a stress test analysis to measure the robustness of different methods on the imperfect weather input, focusing on both the point forecasts and the 95% prediction intervals. The results indicate that different methods should be considered according to the uncertainty characterizing the weather forecasts.

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Section: Discussionmentioning

confidence: 99%

The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece

2020

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“…Reference ARMA [17][18][19][20][21][22][23] ARIMA [24,25] Grey Method [26][27][28] ANN [29][30][31][32][33] SVM [34][35][36][37][38][39] Hybrid [40][41][42][43][44][45][46][47]…”

Section: Approachmentioning

confidence: 99%

“…The work [47] proposed a short-term WPF model that combined three different statistical methods, the Autoregressive Integrated Moving Average with Exogenous variables (ARIMAX), the Support Vector Regression (SVR) and the Monte-Carlo Simulation (MCS) power curve model. The data used were wind power output data and wind speed data from local NWP.…”

Section: Hybrid Approachmentioning

confidence: 99%

Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

Bazionis

Georgilakis

2021

Electricity

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The need to turn to more environmentally friendly sources of energy has led energy systems to focus on renewable sources of energy. Wind power has been a widely used source of green energy. However, the wind’s stochastic and unpredictable behavior has created several challenges to the operation and stability of energy systems. Forecasting models have been developed and excessively used in recent decades in order to deal with these challenges. Deterministic forecasting models have been the main focus of researchers and are still being developed in order to improve their accuracy. Furthermore, in recent years, in order to observe and study the uncertainty of forecasts, probabilistic forecasting models have been developed in order to give a wider view of the possible prediction outcomes. Advanced probabilistic and deterministic forecasting models could be used in order to facilitate the energy systems operation and energy markets management. This paper introduces an overview of state-of-the-art wind power deterministic and probabilistic models, developing a comparative evaluation between the different models reviewed, identifying their advantages and disadvantages, classifying and analyzing current and future research directions in this area.

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“…Thereafter, the components obtained in the previous step (IMFs and one residue component) are trained using extreme learning machines (ELM) [42], SVR [43], Gaussian process (GP) [44], and gradient boosting machines (GBM) [45]. These individual models are chosen due to the effects already observed for regression and time series forecasting tasks, as described in [46][47][48].…”

Section: Objective and Contributionmentioning

confidence: 99%

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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An Ensemble Forecasting Model of Wind Power Outputs Based on Improved Statistical Approaches

Cited by 30 publications

References 26 publications

The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece

The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece

Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

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