Approaches for multi-step density forecasts with application to aggregated wind power

TL;DR

This paper compares two computationally efficient methods for multi-step density forecasting of aggregated wind power, demonstrating their relative accuracy and robustness in practical applications.

Contribution

It introduces two novel approaches for multi-step density forecasting that avoid intensive Monte Carlo simulations, using data transformations and exponential smoothing techniques.

Findings

The first approach slightly outperforms the second in forecast accuracy.

The second approach is more computationally efficient and robust.

Both methods are effective for wind power forecast horizons from 15 minutes to 24 hours.

Abstract

The generation of multi-step density forecasts for non-Gaussian data mostly relies on Monte Carlo simulations which are computationally intensive. Using aggregated wind power in Ireland, we study two approaches of multi-step density forecasts which can be obtained from simple iterations so that intensive computations are avoided. In the first approach, we apply a logistic transformation to normalize the data approximately and describe the transformed data using ARIMA--GARCH models so that multi-step forecasts can be iterated easily. In the second approach, we describe the forecast densities by truncated normal distributions which are governed by two parameters, namely, the conditional mean and conditional variance. We apply exponential smoothing methods to forecast the two parameters simultaneously. Since the underlying model of exponential smoothing is Gaussian, we are able to obtain multi-step forecasts of the parameters by simple iterations and thus generate forecast densities as truncated normal distributions. We generate forecasts for wind power from 15 minutes to 24 hours ahead. Results show that the first approach generates superior forecasts and slightly outperforms the second approach under various proper scores. Nevertheless, the second approach is computationally more efficient and gives more robust results under different lengths of training data. It also provides an attractive alternative approach since one is allowed to choose a particular parametric density for the forecasts, and is valuable when there are no obvious transformations to normalize the data.