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首页> 外文期刊>Sustainability >Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis
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Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

机译：每日平均风电间隔预测基于最优自适应网络的模糊推理系统和奇异频谱分析

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摘要

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.
机译：风能越来越多地在减轻传统资源短缺方面发挥了至关重要的作用。尽管如此，在试图寻找准确的风力预测模型时，风的随机性质会产生巨大的挑战。因此，精确的风力预测在越来越重要的风力情景中解决运营，规划和经济问题的重要性。以前的研究已经致力于努力对风电价值的确定性预测，但是向提供有关风能的信息而少注意。基于基于最佳的自适应网络的模糊推理系统（ANFIS）和奇异频谱分析（SSA），本文开发了混合不确定性预测模型，IFASF（间隔预测 - ANFIS-SSA-FiRogorithm），以获得鞋帮和每日平均风力电力的下限有利于网格公司和独立电力生产商的实际运行。为了加强这一开发模型的实用能力，本文介绍了IFASF和其他基准之间的比较，这为此方面提供了统计或人工智能间隔预测方法的一般参考。比较结果表明，开发的模型优于八个基准，并在三个不同的风电场中具有令人满意的预测效果，具有两次的两次视野。

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