利用CFS气候预报模式预测Três Marias水库( o Francisco河流域)月流量

Pub Date : 2020-01-01 DOI:10.1590/2318-0331.252020190067
Luana Ferreira Gomes De Paiva, S. Montenegro, M. Cataldi
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引用次数: 7

摘要

尽管2016年发生了水危机,但巴西76%的能源是由水力发电厂产生的,这表明巴西的系统仍然强烈依赖流域的水文条件。因此,这些电厂的流量预测补贴了电力部门范围内的决策,因为它们允许通过使用能源优化模型对水力发电厂和热电厂的运行条件进行评估,从而为巴西国家互联系统(Sistema Interligado Nacional)的运行提供收益。降水预报是水电流量预报的基础。对于能量评估,使用DECOMP和NEWAVE模型,使用GEVAZP模型通过AR (p)(自回归)模型生成情景。因此,本研究显示了在气候视界上降水预报对流量预报的影响。为此,我们对气候预报系统(CFS)模式预测的雨量进行了统计校正,并校正了雨量流模式,因为CFS模式往往高估了预测的雨量。对该建模系统进行了试验,并将模拟结果以情景的形式与GEVAZP模型生成的情景进行了比较,表明GEVAZP模型有可能减少生成的范围,从而使DECOMP模型不考虑发生概率很小或没有发生概率的范围,从而提高了sins运行规划的优化程度。这项工作还表明,与神经网络模型相比,SMAP模型在预测与观测流量相关的平均流量范围方面表现出更好的性能。在模拟中加入了一个月前观测到的降雨后,流量预测有了明显的改善,主要是在预测高流量方面。最后,气候指数与流量和雨量变量有良好的关系。
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Prediction of monthly flows for Três Marias reservoir (São Francisco river basin) using the CFS climate forecast model
ABSTRACT Despite the water crisis in 2016, 76% of the energy in Brazil was generated by hydroelectric plants, which shows that the Brazilian system is still strongly dependent on the hydrological conditions of basins. Therefore, the flow forecasts for these plants subsidize the decision making within the scope of the Electric Sector, since they allow the evaluation of the operational conditions of the hydroelectric and thermoelectric plants through the use of energy optimization models, providing gains in the operations of SIN (Sistema Interligado Nacional – the Brazilian National Interconnected System). The precipitation forecast is of fundamental importance for the elaboration of these hydroelectric flow forecasts. For energy evaluations, the DECOMP and NEWAVE models are used, with the GEVAZP model being applied to generate scenarios through an AR (p) (autoregressive) model. Accordingly, this study shows the impact of precipitation forecast on flow predictions in the climate horizon. For this, a statistical correction was made in the rain predicted by the CFS (Climate Forecast System) model, which tends to overestimate the predicted rain, with rainfall-flow models being calibrated. Tests were performed with this new modeling system and the results, in the form of scenarios, were compared with the scenarios generated by the GEVAZP model, showing the possibility of reducing the generated range by the latter, consequently causing the DECOMP model to not consider ranges with little or no probability of occurrence, which can improve the optimization of the SIN operation planning. This work also shows that the SMAP model exhibited better performance when compared to the Neural Networks model, in terms of the average flow range predicted in relation to the observed flow. There was a clear improvement in the flow predictions with the incorporation of the rain observed one month ahead in the simulations, mainly in the forecast of high flows. Finally, the climate indices had a good relationship with the flow and rain variables.
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