Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques

Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system. As the relationship between wind and the generated power is highly nonlinear and time-varying, and given the increasing number of available forecasting techniques, it is possible to use alternative models to obtain more than one prediction for the same hour and forecast horizon. To increase forecast accuracy, it is possible to combine the different predictions to obtain a better one or to dynamically select the best one in each time period. Hybrid alternatives based on combining a few selected forecasts can be considered when the number of models is large. One of the most popular ways to combine forecasts is to estimate the coefficients of each prediction model based on its past forecast errors. As an alternative, we propose using multivariate reduction techniques and Markov chain models to combine forecasts. The combination is thus not directly based on the forecast errors. We show that the proposed combination strategies based on dimension reduction techniques provide competitive forecasting results in terms of the Mean Square Error.

Prognóstico da série histórica do faturamento da indústria alimentícia brasileira utilizando técnicas de previsão

This paper’s objective is to verify which is the best forecasting technique, including the use of the forecasts’ combination to evaluate the prognosis of the Brazilian food industry’s revenues. The historical series of revenues has deterministic trend and seasonality. Thereby, the models chosen to work on were: SARIMA (3,0,0)×(0,1,1)12, SARIMA (4,0,0)×(2,0,0)12 and Holt-Winters Multiplicative. Analyzing the accuracy measures, to perform the series’ forecast it was used the combination of the three models, presented by the methods: Simple Arithmetic Mean, Ordinary Least Squares and Regression of Absolute Minimum Deviation. The results obtained by the forecast were satisfactory, showing that the Brazilian food industry’s revenues will have peaks of growth and decay in the next two years. Therefore, a preparation of the sector is necessary for the period in which a possible decrease in this revenue will occur, as well as dismissal of the workers, since it is the sector that most employs in Brazil.

Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods

Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance.

A novel model independence methodology to improve multi-model seasonal forecasts combination

<p align="justify">Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single model ensembles. The potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. To this aim, a novel methodology has been developed to assess the relative independence of the prediction systems in the probabilistic information they provide.</p><p align="justify"><span>We considered the Copernicus C3S seasonal forecasts product considering the one-month lead retrospective seasonal predictions for boreal summer and boreal winter seasons (1</span><sup><span>st</span></sup><span> May and 1</span><sup><span>st</span></sup><span> November start dates, i.e. June-July-August, JJA and December-January-February, DJF). We analysed the seasonal hindcasts in terms of deterministic and probabilistic scores with a particular focus on </span><span>continental areas</span><span>, since little evaluation has been performed so far over land domains that is where most of the applications of seasonal forecasts are based. The most relevant target variables of interest for the energy users have been considered and skill differences between the prediction systems have been analysed together with related possible sources of predictability. The analysis evidenced the importance of snow-albedo processes for temperature predictions in DJF and the effect of the atmospheric dynamics through moisture convergence for the prediction of surface solar radiation in JJA. </span><span>A</span><span> new metric, the Brier Score Covariance, designed to quantify the probabilistic independence among the models, has been </span><span>developed and </span><span>applied to optimize model selection and combination strategies with a particular focus on the most relevant variables for energy applications.</span></p>