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The influence of rainfall time series fractality on forecasting models’ efficiency

Rahmani Farhang, Fattahi Mohammad Hadi

Acta Geophysica

2022

Vol. 70, no 3

1349--1361

Forecasting rainfall time series is of great significance for hydrologists and geoscientists. Thus, this study represents a contribution to understanding the impact of the fractal time series variety on forecasting model performance. Multiple fractal series were generated via p-model and used for modeling. Subsequently, the forecasting was delivered based on existing observed monthly rainfall data (three stations in the UK, from 1865 to 2002) through five forecasting models. Finally, the association between series fractality and models’ performance was examined. The results indicated that the forecasting based on the mono-fractal series resulted in the most reliable results (R2=1 and RMSE less than 0.02). In the case of multifractal series, modeling based on series with the right side of the asymmetric curve of the multifractal spectrum presented series with the lowest RMSE (0.96) and highest R2 (0.99) (desirable performance). In contrast, the forecasting based on series with the left side of the asymmetric curve of the multifractal spectrum suggested the most unreliable outcomes (R2 range [−0.0007 ~ 0.988] and RMSE range [0.8526 ~ 39.3]). The forecasting based on the symmetric curve of the multifractal spectrum series delivered regular performance. Accordingly, high and low errors are expected from forecasting based on the time series with a left-skewed multifractal spectrum and right-skewed multifractal spectrum (and mono-fractal time series), respectively. Hybrid models were the best options for forecasting mono-fractal and multifractal time series with right side asymmetric and symmetric multifractal spectrum curves. The ARIMA model was suitable to predict multifractal time series with left side asymmetric multifractal spectrum curves.

Proposing an efficient wind forecasting agent using adaptive MFDFA

Mozaffarilegha Mahdi, Farjah Ebrahim

Journal of Power Technologies

2019

Vol. 99, nr 2

152--162

High penetration by distributed energy sources (DERs) such as wind turbines (WT) and various types of consumer have triggered a need for new approach to coordination and control strategy to meet the stochastic wind speed of the environment. Here, a Multi Agent System is used to deliver strengthened, distributed, self-governing energy management of a multiple micro-grid to adapt to changes in the environment. Prediction of wind speed is crucial for various aspects, such as control and planning of wind turbine operation and guaranteeing stable performance of multiple micro-grids. The main purpose of the proposed system is to account for wind variability in the energy management of a multiple micro-grid based on a hierarchical multi-factor system. In this study, the prediction is based on adaptive multifractal detrended fluctuation analysis (Adaptive MFDFA). A genetic algorithm is used to solve the optimization problem. Eventually, the proposed strategy is applied to a typical MG which consists of micro turbine (MT), wind turbine (WT) and energy storage system (ESS). Evaluation of the results show that the proposed strategy works well and can adapt the level of confidence interval in various situations.