Arbitrated Ensemble for Solar Radiation Forecasting

Cerqueira, Vítor; Torgo, Luı́s; Soares, Carlos

doi:10.1007/978-3-319-59153-7_62

Cited by 8 publications

(5 citation statements)

References 24 publications

(26 reference statements)

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“…Arbitrating [36] uses the metalearning method to learn and predict the classifiers. In this study, it regards the weights based on each model's performance for a given time step.…”

Section: Arbitrating Methodsmentioning

confidence: 99%

Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions

Vidal Bezerra,

Pinto Marinho,

Costa Rocha

et al. 2023

Atmosphere

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This paper proposes to analyze the performance increase in the forecasting of solar irradiance and wind speed by implementing a dynamic ensemble architecture for intra-hour horizon ranging from 10 to 60 min for a 10 min time step data. Global horizontal irradiance (GHI) and wind speed were computed using four standalone forecasting models (random forest, k-nearest neighbors, support vector regression, and elastic net) to compare their performance against two dynamic ensemble methods, windowing and arbitrating. The standalone models and the dynamic ensemble methods were evaluated using the error metrics RMSE, MAE, R2, and MAPE. This work’s findings showcased that the windowing dynamic ensemble method was the best-performing architecture when compared to the other evaluated models. For both cases of wind speed and solar irradiance forecasting, the ensemble windowing model reached the best error values in terms of RMSE for all the assessed forecasting horizons. Using this approach, the wind speed forecasting gain was 0.56% when compared with the second-best forecasting model, whereas the gain for GHI prediction was 1.96%, considering the RMSE metric. The development of an ensemble model able to provide accurate and precise estimations can be implemented in real-time forecasting applications, helping the evaluation of wind and solar farm operation.

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“…Arbitrating [36] uses the metalearning method to learn and predict the classifiers. In this study, it regards the weights based on each model's performance for a given time step.…”

Section: Arbitrating Methodsmentioning

confidence: 99%

Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions

Vidal Bezerra,

Pinto Marinho,

Costa Rocha

et al. 2023

Atmosphere

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“…This work follows a first attempt on adapting the arbitration approach to numerical prediction tasks. This strategy was applied to solar radiation forecasting tasks, and the arbitration methodology is extended by weighing all forecasting models, as opposed to selecting the one with lowest predicted error [6]. In this paper we introduce other components to arbitrating.…”

Section: Metalearning Strategies For Model Combinationmentioning

confidence: 99%

Arbitrated Ensemble for Time Series Forecasting

Cerqueira

Torgo

Pinto

et al. 2017

Lecture Notes in Computer Science

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This paper proposes an ensemble method for time series forecasting tasks. Combining different forecasting models is a common approach to tackle these problems. State-of-the-art methods track the loss of the available models and adapt their weights accordingly. Metalearning strategies such as stacking are also used in these tasks. We propose a metalearning approach for adaptively combining forecasting models that specializes them across the time series. Our assumption is that different forecasting models have different areas of expertise and a varying relative performance. Moreover, many time series show recurring structures due to factors such as seasonality. Therefore, the ability of a method to deal with changes in relative performance of models as well as recurrent changes in the data distribution can be very useful in dynamic environments. Our approach is based on an ensemble of heterogeneous forecasters, arbitrated by a metalearning model. This strategy is designed to cope with the different dynamics of time series and quickly adapt the ensemble to regime changes. We validate our proposal using time series from several real world domains. Empirical results show the competitiveness of the method in comparison to state-of-the-art approaches for combining forecasters.

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“…In this context, heterogeneity among base learners leads to an improvement of the overall predictive ability of the ensemble (e.g. [6], [1], [21]).…”

Section: A Dynamic Heterogeneous Ensemblementioning

confidence: 99%

Dynamic and Heterogeneous Ensembles for Time Series Forecasting

Cerqueira

Torgo

Oliveira

et al. 2017

2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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This paper addresses the issue of learning time series forecasting models in changing environments by leveraging the predictive power of ensemble methods. Concept drift adaptation is performed in an active manner, by dynamically combining base learners according to their recent performance using a non-linear function. Diversity in the ensembles is encouraged with several strategies that include heterogeneity among learners, sampling techniques and computation of summary statistics as extra predictors. Heterogeneity is used with the goal of better coping with different dynamic regimes of the time series. The driving hypotheses of this work are that (i) heterogeneous ensembles should better fit different dynamic regimes and (ii) dynamic aggregation should allow for fast detection and adaptation to regime changes. We extend some strategies typically used in classification tasks to time series forecasting. The proposed methods are validated using Monte Carlo simulations on 16 realworld univariate time series with numerical outcome as well as an artificial series with clear regime shifts. The results provide strong empirical evidence for our hypotheses. To encourage reproducibility the proposed method is publicly available as a software package.

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Arbitrated Ensemble for Solar Radiation Forecasting

Cited by 8 publications

References 24 publications

Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions

Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions

Arbitrated Ensemble for Time Series Forecasting

Dynamic and Heterogeneous Ensembles for Time Series Forecasting

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