Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predicting and quantifying the effect of variations in long-term water demand on micro-hydropower energy recovery in water supply networks
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.
Predicting and quantifying the effect of variations in long-term water demand on micro-hydropower energy recovery in water supply networks
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.
Predicting and quantifying the effect of variations in long-term water demand on micro-hydropower energy recovery in water supply networks
Corcoran, Lucy (Autor:in) / McNabola, Aonghus / Coughlan, Paul
Urban water journal ; 14
2017
Aufsatz (Zeitschrift)
Englisch
Energy efficiency , Economic conditions , sustainability , Pressure , Feasibility studies , Feasibility , Demand (economics) , Climate , long-term forecasting , water supply , Water supply , Locations (working) , Regressions , Artificial neural network , hydropower , Flow velocity , Long term changes , water demand , Power efficiency , Water demand , Artificial neural networks , Water supply systems , Water flow , Turbines , Water shortages , Energy recovery , Neural networks , Historical account , Design , Efficiency , Energy management , regression , Hydroelectric power , Energy , Flow rates
Energy recovery potential using micro hydropower in water supply networks in the UK and Ireland
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