Two Machine Learning Approaches for Short-Term Wind Speed Time Series Prediction
5. CONCLUSION
Effective management of smart grids includes several elements of distributed intelligent control on the supplier and the consumer sides. On the supplier side, a successful integration of renewable power sources and handling of the associated uncertainties are pivotal for the reliability of the power network.
For wind power in particular, a crucial element is to have accurate and stable predictions of wind speeds, concurrently quantifying the associated uncertainties of the predictions. .
In this paper, we have proposed and compared two machine learning approaches, MOGA NN and ELM combined with the nearest neighbors approach for estimating prediction intervals.
The algorithms have been applied on a case study of short-term wind speed prediction using a real dataset of hourly wind speed measurements.
Contrary to classical time-series prediction approaches, both proposed approaches generate prediction intervals for the target of interest. Knowledge of PIs allows the decision makers and operational planners to efficiently quantify the level of uncertainty associated with the forecasts and to consider a multiplicity of solutions/scenarios for the best and worst conditions.
Both algorithms show a good accuracy and generalization ability on the conducted case study.
Paper III-R. Ak, O. Fink and E. Zio (2014), submitted to Special Issue on Neural Networks and Learning Systems Applications in Smart Grid (under review).
158 The results do not show significant differences in terms of the quality of the predicted PIs. We can conclude that both methods yield a reliable estimation of the PIs with a high coverage and a relatively small interval size.
The approaches to estimate the PIs are based on very different concepts and can be selected depending on the specific requirements of the user, including quality of the results, generalization ability, computational efficiency, flexibility and ease of use.
Generally, if an algorithm is specifically trained to optimize two objectives, it is expected to be superior to an algorithm that was trained to optimize a simple error criterion. However, this could not be observed in this research. The presented results indicate that the generalization ability of the ELM on the training dataset is representative of the performance on new data patterns and multi-objective optimization is, therefore, not required in this case.
Both applied algorithms are data-driven and depend highly on the representativeness of the training dataset. Therefore, the quality of PIs can decrease on datasets with large variability and uncertainty in the data.
A possible direction of future research is to implement online learning algorithms that are able to adjust their parameters while novel patterns evolve, without retraining the whole algorithm.
This would be particularly useful for applications, in which the available dataset is too short to cover all possible patterns or in which the environmental or operational conditions change.
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