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Predicting and quantifying the effect of variations in long-term water demand on micro-hydropower energy recovery in water supply networks

机译:预测和量化长期用水需求变化对供水网络中微水电能量回收的影响

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To improve water supply energy efficiency micro-hydropower turbines can be installed within networks at locations of excess pressure. However, future changes in flow rates and pressures at these locations could render an installed turbine unsuitable. It is therefore important that long term changes in flow conditions at potential turbine locations be considered at initial feasibility/design stages.Using historical data over a ten-year period, this paper predicts the effects of changes in water flow rates at potential turbine locations in Ireland and the UK. Results show that future flow rates at these locations could be predicted with an R-2 of up to 66% using multivariate linear regression and up to 93% using artificial neural networks. Flow rates were shown to vary with population, economic activity and climate factors. Changes in flow rate were shown to have a significant impact on power output within the design life of a typical hydropower turbine.
机译:为了提高供水能效,可以将微型水力涡轮机安装在网络中压力过大的位置。但是,未来这些位置的流速和压力的变化可能会导致安装的涡轮机不合适。因此,重要的是在最初的可行性/设计阶段就应考虑潜在涡轮机位置的长期流量变化。本文使用十年来的历史数据,预测了潜在涡轮机位置水流量变化的影响。爱尔兰和英国。结果表明,使用多元线性回归可以预测这些位置的未来流速,R-2可达66%,而人工神经网络的R-2可达93%。流量随人口,经济活动和气候因素而变化。在典型的水力涡轮机的设计寿命内,流量的变化显示出对功率输出有重大影响。

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