Development of a real-time forecasting model for demand and levels of reservoir with big data of water industry

• Grant number: 18/26592-5
• Support Opportunities: Scholarships in Brazil - Master
• Start date: March 01, 2019
• End date: February 28, 2021
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Chang Chiann
• Grantee: Leonardo Fonseca Larrubia
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 17/50343-2 - Institutional development plan in the area of digital transformation: advanced manufacturing and smart and sustainable cities (PDIp), AP.PDIP

Abstract

The objective of this project is to explore a big data solution of the water industry for the unattended automation of the daily programming of the water adduction operational controls using some parametric time series models such as harmonic model with periodicity of 12 and 24 hours + autoregressive model; SARIMA model (Seasonal Autoregressive Moving Average Models); model ARFIMA (Long Memory ARMA and Fractional Differencing) and regression model using wavelets. (AU)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

LARRUBIA, Leonardo Fonseca. Anomaly detection, interpolation and real-time forecasting of time series for reservoir operation and water distribution. 2021. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI) São Paulo.