Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city

As part of sustainable urban planning, the demand for water and energy (WE) should also be addressed. The Waikato Environment for Knowledge Analysis (WEKA) modeling tool was employed to relate the historical WE consumptions with the population and economic growth scenarios using a linear regression...

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Autores principales:	Bedassa Dessalegn Kitessa, Semu Moges Ayalew, Geremew Sahilu Gebrie, Solomon Tesfamariam Teferi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	consumption demand energy regression water weka Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/5e556471a7da402a9c7b54209aaa8037
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spelling	oai:doaj.org-article:5e556471a7da402a9c7b54209aaa80372021-11-05T19:07:45ZLong-term water-energy demand prediction using a regression model: a case study of Addis Ababa city2040-22442408-935410.2166/wcc.2021.012https://doaj.org/article/5e556471a7da402a9c7b54209aaa80372021-09-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/6/2555https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354As part of sustainable urban planning, the demand for water and energy (WE) should also be addressed. The Waikato Environment for Knowledge Analysis (WEKA) modeling tool was employed to relate the historical WE consumptions with the population and economic growth scenarios using a linear regression model. The performance of the model was evaluated to properly identify the most influential drivers in each sector. The WE demand prediction was made for each year from 2016 up to 2050. Consequently, the long-term time interval for demand analysis is important rather than the consequent year for planning. The total electric energy demand including residential, street-lighting, commercial and industrial sectors was estimated to be around 14,000 and 53,000 Giga Watt hour (GWh) for the years 2030 and 2050, respectively. These years' forecasted petroleum demand was around 8840 and 30,140 for diesel, 13,860 and 52,700 for gasoline, and 1230 and 9890 GWh for kerosene and the water demand including residential, commercial and industrial sectors were 520 and 1600 million cubic meters (MCM). The proposed methodology can comfortably be used to predict the urban WE demand corresponding to economic (gross domestic product and per capita income) and population growth at different scenarios which could support policy makers. HIGHLIGHTS Predicting long-term water-energy demand is important for planning.; A linear regression model is used for long-term water-energy demand predicting.; The water-energy demand in urban areas is increasing.; Population and economic growth are the main factors which are highly affecting the urban water-energy demand.; Identifying the most influential drivers on water-energy demand is important for water-energy supply planning.; Technological factors (such as water loss, energy loss) are commonly considered in demand prediction.;Bedassa Dessalegn KitessaSemu Moges AyalewGeremew Sahilu GebrieSolomon Tesfamariam TeferiIWA PublishingarticleconsumptiondemandenergyregressionwaterwekaEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 6, Pp 2555-2578 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	consumption demand energy regression water weka Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	consumption demand energy regression water weka Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Bedassa Dessalegn Kitessa Semu Moges Ayalew Geremew Sahilu Gebrie Solomon Tesfamariam Teferi Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city
description	As part of sustainable urban planning, the demand for water and energy (WE) should also be addressed. The Waikato Environment for Knowledge Analysis (WEKA) modeling tool was employed to relate the historical WE consumptions with the population and economic growth scenarios using a linear regression model. The performance of the model was evaluated to properly identify the most influential drivers in each sector. The WE demand prediction was made for each year from 2016 up to 2050. Consequently, the long-term time interval for demand analysis is important rather than the consequent year for planning. The total electric energy demand including residential, street-lighting, commercial and industrial sectors was estimated to be around 14,000 and 53,000 Giga Watt hour (GWh) for the years 2030 and 2050, respectively. These years' forecasted petroleum demand was around 8840 and 30,140 for diesel, 13,860 and 52,700 for gasoline, and 1230 and 9890 GWh for kerosene and the water demand including residential, commercial and industrial sectors were 520 and 1600 million cubic meters (MCM). The proposed methodology can comfortably be used to predict the urban WE demand corresponding to economic (gross domestic product and per capita income) and population growth at different scenarios which could support policy makers. HIGHLIGHTS Predicting long-term water-energy demand is important for planning.; A linear regression model is used for long-term water-energy demand predicting.; The water-energy demand in urban areas is increasing.; Population and economic growth are the main factors which are highly affecting the urban water-energy demand.; Identifying the most influential drivers on water-energy demand is important for water-energy supply planning.; Technological factors (such as water loss, energy loss) are commonly considered in demand prediction.;
format	article
author	Bedassa Dessalegn Kitessa Semu Moges Ayalew Geremew Sahilu Gebrie Solomon Tesfamariam Teferi
author_facet	Bedassa Dessalegn Kitessa Semu Moges Ayalew Geremew Sahilu Gebrie Solomon Tesfamariam Teferi
author_sort	Bedassa Dessalegn Kitessa
title	Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city
title_short	Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city
title_full	Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city
title_fullStr	Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city
title_full_unstemmed	Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city
title_sort	long-term water-energy demand prediction using a regression model: a case study of addis ababa city
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/5e556471a7da402a9c7b54209aaa8037
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Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city

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