Intelligent water service daily water consumption prediction method based on machine learning

Abstract

The invention relates to the technical field of big data processing in machine learning, in particular to an intelligent water service daily water consumption prediction method based on machine learning, which comprises the following steps: obtaining date, climate data and historical water consumption data, and preprocessing; inputting the preprocessed historical water consumption data into a TCNprediction model to extract chaotic information and hidden features for prediction to obtain a TCN prediction result; correcting the TCN prediction result through the date and climate data, and inputting the date, climate data and the TCN prediction result together as features into a machine learning model for training and prediction to obtain a corrected next-day water consumption prediction result. According to the invention, machine learning and urban daily water demand prediction are combined, and the urban daily water demand can be accurately predicted.

Description

technical field [0001] The invention relates to the technical field of big data processing in machine learning, in particular to a method for predicting daily water consumption of smart water affairs based on machine learning. Background technique [0002] As the world's population grows, the climate warms, and cities expand in size, cities place ever-increasing demands on water resources. Many countries are facing the problem of water shortage, so the rational planning and management of water resources is particularly important. A prerequisite for this to happen is a reliable forecast of daily water demand. Daily water demand forecasting plays an important role in urban construction planning and optimal scheduling of water distribution systems. Additionally, it could help city planners make better decisions about how to allocate water efficiently. Daily water consumption depends on various factors such as date, climate, social factors, etc. Climate factors are becoming ...

AI Technical Summary

Problems solved by technology

[0003] Since the daily water consumption is closely related to external factors, the current model generally has the following technical problems: 1. With the increasing size of the city and the increase in water demand, the factors affecting urban water demand are complex and changeable, and the establishment of urban water demand Deterministic and uncertain variables are very difficult

3. There is no systematic feature engineering on the city’s daily water demand and its influencing factors

4. Single model

Not optimized for the model

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