B Du, Q Zhou, J Guo, S Guo, L Wang - Expert Systems with Applications, 2021 - Elsevier
… and catching the peaks in time series, … dailywaterdemand data are collected from 1st January 2016 to 11th September 2020, with the first 998 daily data are used for training the model …
… reported cumulative values of waterconsumption, generating misleading peaks. These outliers … This study uses a feed forward neuralnetwork with input, hidden, and output layers. The …
… the correlation coefficient between waterconsumption and maximum temperature time series (from 0.63 to 0.93). Additionally, the correlation coefficient between the stochastic signal of …
L Mu, F Zheng, R Tao, Q Zhang… - Journal of Water …, 2020 - ascelibrary.org
… of models are available for urban waterdemandforecasts … These include artificialneural networks (ANNs) that have been … RF models, especially when the dailymaximum temperature ( …
… model was run several times to find the best neuralnetwork architecture to forecast municipal waterdemand. … that maximum temperature, radiation and rain, are reliable predictors when …
B Du, S Huang, J Guo, H Tang, L Wang, S Zhou - Applied Soft Computing, 2022 - Elsevier
… 1660 dailywaterdemand and other variables of a water plant … The waterdemand data is divided into three parts, which are … -BP model has the maximum bias of point prediction among …
… with waterusage data is proposed. This study uses 51 days of waterconsumption readings … To ensure maximum efficiency in data processing, a spatiotemporal database was created …
… The pricebased DR programs include critical peak pricing scheme (CPPS), time of use … In this section, the simulation results and discussion are presented to validate the performance of …
… From the descriptive statistics (see Table 6), the proposed model’s mean, standard deviation, minimum and maximum values are almost the same as the actual data values than the …