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

被引:12
作者
Corcoran, Lucy [1 ]
McNabola, Aonghus [1 ]
Coughlan, Paul [2 ]
机构
[1] Trinity Coll Dublin, Dept Civil Struct & Environm Engn, Dublin, Ireland
[2] Trinity Coll Dublin, Sch Business, Dublin, Ireland
关键词
Artificial neural network; hydropower; long-term forecasting; regression; sustainability; water demand; water supply; DESIGN; SYSTEM; UK;
D O I
10.1080/1573062X.2016.1236136
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
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.
引用
收藏
页码:676 / 684
页数:9
相关论文
共 35 条
[1]   Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada [J].
Adamowski, Jan ;
Chan, Hiu Fung ;
Prasher, Shiv O. ;
Ozga-Zielinski, Bogdan ;
Sliusarieva, Anna .
WATER RESOURCES RESEARCH, 2012, 48
[2]   Water consumption prediction of Istanbul City by using fuzzy logic approach [J].
Altunkaynak, A ;
Özger, M ;
Çakmakci, M .
WATER RESOURCES MANAGEMENT, 2005, 19 (05) :641-654
[3]  
Arbues F., 2003, J SOCIO-ECON, V32, P81, DOI DOI 10.1016/S1053-5357(03)00005-2
[4]   Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok [J].
Babel, Mukand Singh ;
Shinde, Victor R. .
WATER RESOURCES MANAGEMENT, 2011, 25 (06) :1653-1676
[5]   Modeling water use in schools: A disaggregation approach [J].
Barua, S. ;
Ng, A. W. M. ;
Muthukumaran, S. ;
Roberts, P. ;
Perera, B. J. C. .
URBAN WATER JOURNAL, 2016, 13 (08) :875-881
[6]  
Billings B., 2008, FORECASTING URBAN WA
[7]   Short-term municipal water demand forecasting [J].
Bougadis, J ;
Adamowski, K ;
Diduch, R .
HYDROLOGICAL PROCESSES, 2005, 19 (01) :137-148
[8]   Cost-Benefit Analysis for Hydropower Production in Water Distribution Networks by a Pump as Turbine [J].
Carravetta, Armando ;
Fecarotta, Oreste ;
Sinagra, Marco ;
Tucciarelli, Tullio .
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2014, 140 (06)
[9]   Pump as Turbine ( PAT) Design in Water Distribution Network by System Effectiveness [J].
Carravetta, Armando ;
Del Giudice, Giuseppe ;
Fecarotta, Oreste ;
Ramos, Helena M. .
WATER, 2013, 5 (03) :1211-1225
[10]   Energy Production in Water Distribution Networks: A PAT Design Strategy [J].
Carravetta, Armando ;
Del Giudice, Giuseppe ;
Fecarotta, Oreste ;
Ramos, Helena M. .
WATER RESOURCES MANAGEMENT, 2012, 26 (13) :3947-3959