Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Farías, Rodrigo López; Puig, Vicenç; Rangel, Héctor Rodríguez; Flores, Juan J.

doi:10.3390/en11030660

Cited by 28 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The water consumption dataset is categorized into the training and validation datasets for forecasting water demand. In general, the training dataset uses the entire observed values of 70-90%; however, in this study, among the water consumption data of one year measured at one-hour intervals, 70% of the data were used as the training dataset overtime sequence, whereas the remaining 30% were used as the validation dataset [31]. The water consumption data are standardized for reducing the bias in demand forecasting.…”

Section: Study Processmentioning

confidence: 99%

“…Rangel et al [29] used the concept of daily water consumption pattern predicted based on Nearest Neighbor (NN) node estimation, which is a nonparametric method; Cheifetz et al [30] estimated the water demand pattern for each day of the week using the hourly water consumption data based on the Fourier regression mixture model. Particularly, Farias et al [31] applied the NN classification, which is an ML model, and a calendar effect based on quantitative and qualitative information, and proposed the Qualitative Multi-Model Predictor Plus (QMMP+) model for estimating water demand patterns based on the moving average (MA). Here, better results were produced when the total daily water demand was forecasted using the SARIMA model applied with a sliding window [26], whereas the hourly water demand was distributed with the NN model according to a calendar effect for daily patterns and compared with the ANN model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea

et al. 2021

View full text Add to dashboard Cite

It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real-time through a smart meter, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include Autoregressive Integrated Moving Average, Radial Basis Function-Artificial Neural Network, Quantitative Multi-Model Predictor Plus, and Long Short-Term Memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand in the SWG demonstration plant. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from a smart meter, and the performance of each model was assessed. The Smart Water Grid Research Group installed a smart meter in block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the Residual, Root Mean Square Error, Normalized Root Mean Square Error, Nash–Sutcliffe Efficiency, and Pearson Correlation Coefficient as indices. As a result of water demand forecasting, it is difficult to forecast water demand only by time and water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

show abstract

Section: Study Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Their study results were found to be satisfactory in a comparison with other used techniques in the literature. In addition, Farias, Puig, Rangel and Flores [25] attempted to forecast the demand of water distribution networks by deploying a multi-model predictor, qualitative multi-model predictor plus (QMMP+). In their study, it was found that such a predictor enhances the forecasting precision.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

Sutthichaimethee

Kubaha

2018

Sustainability

View full text Add to dashboard Cite

The Thailand Development Policy focuses on the simultaneous growth of the economy, society, and environment. Long-term goals have been set to improve economic and social well-being. At the same time, these aim to reduce the emission of CO 2 in the future, especially in the construction sector, which is deemed important in terms of national development and is a high generator of greenhouse gas. In order to achieve national sustainable development, policy formulation and planning is becoming necessary and requires a tool to undertake such a formulation. The tool is none other than the forecasting of CO 2 emissions in long-term energy consumption to produce a complete and accurate formulation. This research aims to study and forecast energy-related carbon dioxide emissions in Thailand's construction sector by applying a model incorporating the longand short-term auto-regressive (AR), integrated (I), moving average (MA) with exogenous variables (Xi) and the error correction mechanism (LS-ARIMAXi-ECM) model. This model is established and attempts to fill the gaps left by the old models. In fact, the model is constructed based on factors that are causal and influential for changes in CO 2 emissions. Both independent variables and dependent variables must be stationary at the same level. In addition, the LS-ARIMAXi-ECM model deploys a co-integration analysis and error correction mechanism (ECM) in its modeling. The study's findings reveal that the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model is a forecasting model with an appropriate time period (t − i), as justified by the Q-test statistic and is not a spurious model. Therefore, it is used to forecast CO 2 emissions for the next 20 years (2019 to 2038). From the study, the results show that CO 2 emissions in the construction sector will increase by 37.88% or 61.09 Mt CO 2 Eq. in 2038. Also, the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model has been evaluated regarding its performance, and it produces a mean absolute percentage error (MAPE) of 1.01% and root mean square error (RMSE) of 0.93% as compared to the old models. Overall, the results indicate that determining future national sustainable development policies requires an appropriate forecasting model, which is built upon causal and contextual factors according to relevant sectors, to serve as an important tool for future sustainable planning.

show abstract

“…An operational MPC approach was proposed by Pascual et al [6], tasked with reducing costs, as well as maintaining safety storage volumes in the buffer tanks. Another control based water management system was proposed by Lopez Farias et al [7]. In this case, the forecasting accuracy of the control system was improved by means of a qualitative multi-model predictor.…”

Section: Introductionmentioning

confidence: 99%

An IoT Architecture for Water Resource Management in Agroindustrial Environments: A Case Study in Almería (Spain)

Muñoz

Gil

Roca

et al. 2020

Sensors

View full text Add to dashboard Cite

The current agricultural water panorama in many Mediterranean countries is composed by desalination facilities, wells (frequently overexploited), the water public utility network, and several consumer agents with different water needs. This distributed water network requires centralized management methods for its proper use, which are difficult to implement as the different agents are usually geographically separated. In this sense, the use of enabling technologies such as the Internet of Things can be essential to the proper operation of these agroindustrial systems. In this paper, an Internet of Things cloud architecture based on the FIWARE standard is proposed for interconnecting the several agents that make up the agroindustrial system. In addition, this architecture includes an efficient management method based on a model predictive control technique, which is aimed at minimizing operating costs. A case study inspired by three real facilities located in Almería (southeast of Spain) is used as the simulation test bed. The obtained results show how around 75% of the total operating costs can be saved with the application of the proposed approach, which could be very significant to decrease the costs of desalinated water and, therefore, to maintain the sustainability of the agricultural system.

show abstract

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Cited by 28 publications

References 26 publications

Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea

Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

An IoT Architecture for Water Resource Management in Agroindustrial Environments: A Case Study in Almería (Spain)

Contact Info

Product

Resources

About