An empirical investigation of Water Consumption Forecasting methods
Research output: Contribution to journal › Article › peer-review
Standard Standard
In: International Journal of Forecasting, Vol. 36, No. 2, 06.2020, p. 588-606.
Research output: Contribution to journal › Article › peer-review
HarvardHarvard
APA
CBE
MLA
VancouverVancouver
Author
RIS
TY - JOUR
T1 - An empirical investigation of Water Consumption Forecasting methods
AU - Karamaziotis, Panagiotis I.
AU - Raptis, Achilleas
AU - Nikolopoulos, Konstantinos
AU - Litsiou, Konstantina
AU - Assimakopoulos, Vassilis
PY - 2020/6
Y1 - 2020/6
N2 - Many regions on earth everyday face limitations in the quantity and quality of available water resources. To that end, it is necessary to implement reliable methodologies for water consumption forecasting, that will lead to better management and planning of water resources. In this research, we analyse a first-time used large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas in Europe, which faces instances of droughts and overconsumption in hot summer months. With the assistance of R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results
AB - Many regions on earth everyday face limitations in the quantity and quality of available water resources. To that end, it is necessary to implement reliable methodologies for water consumption forecasting, that will lead to better management and planning of water resources. In this research, we analyse a first-time used large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas in Europe, which faces instances of droughts and overconsumption in hot summer months. With the assistance of R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results
U2 - 10.1016/j.ijforecast.2019.07.009
DO - 10.1016/j.ijforecast.2019.07.009
M3 - Article
VL - 36
SP - 588
EP - 606
JO - International Journal of Forecasting
JF - International Journal of Forecasting
SN - 0169-2070
IS - 2
ER -