Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

Zhongrong Zhang,Feng Liu,Jinpeng Liu,Yiliao Song

doi:10.3390/su8020125

Abstract

Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Singular Spectrum Analysis (SSA), this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm), to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

Highlights

(1) Singular Spectrum Analysis (SSA) is applied for de-noising daily average wind power; (2) The neighborhood radius of the subtractive clustering algorithm is optimized by the firefly algorithm; (3) Based on the optimal neighborhood radius, SSA and Adaptive-Network-Based Fuzzy Inference System (ANFIS), we develop a hybrid interval forecasting method, IFASF, for daily average wind power; (4) An extensive comparison of the Autoregressive Integrated Moving Average Model (ARIMA), Back
The interval of average wind power data is hard to forecast, ANFIS-SSA and IFASF still give satisfactory results because their IFCPs are more than 70% when used to forecast a 70% interval of wind power
From the average values of each column, IFCPs of IFASF are 40%, 62.29% and 76%, which are higher than the 37.14%, 56.57% and 72% obtained with ANFIS-SSA and the 30.29%, 52% and 65.14% obtained with ANFIS

Summary

Introduction

The current policy trend to move China toward having a larger fraction of its energy portfolio devoted to renewable energy resources puts additional strain on the energy industry, because these sources have, to date, been less predictable than traditional generation sources. Wpn) , which is the mean wind power series, and wpi P [0, C], where n P N and C denotes the installed capacity of the wind farm (it should be clarified that this paper only collects wind power data and there is no other additional data used, which indicates that the input data of the models is only wind power data). The main steps of the proposed model are as demonstrated below. Let d, nt P N, X Ď Rd be the input space constructed by elements in Wtrain and Y Ď R be the output space constructed by elements in Wstrain.

Methods

Results

Conclusion