Water consumption prediction method and device, electronic equipment and computer readable medium

Abstract

The invention discloses a water consumption prediction method and device, and relates to the technical field of artificial intelligence such as deep learning. One specific embodiment of the method comprises the steps of acquiring a historical data set in a preset historical time period, wherein the historical data set comprises holiday data, workday data, temperature, humidity, wind direction, wind power, precipitation and historical water consumption in the preset time period; performing feature construction on the historical data set to extract a time feature sequence, a weather feature sequence and a historical water consumption feature sequence; obtaining a training data set and a test data set based on the constructed historical data set; based on the training data set, training the constructed water consumption prediction model to obtain a trained water consumption prediction model; and predicting the trained water consumption prediction model by adopting the test data set to obtain the water consumption in the future time period output by the trained water consumption prediction model. According to the embodiment, the accuracy of water consumption prediction is improved.

Description

technical field [0001] The present application relates to the technical field of data processing, specifically to the technical field of deep learning, and in particular to a water consumption prediction method, device, electronic equipment, and computer-readable medium. Background technique [0002] The water consumption forecast in the daily life of the water affairs group generally uses network models, such as ARIMA (Autoregressive Integrated Moving Average Model, autoregressive moving average model), RNN (Recurrent Neural Network, cyclic neural network), etc. to predict water sales. The features input to the model include internal features and external features. The internal features include store location, latitude and longitude, etc., and the external features include promotion volume, etc. The accuracy of the prediction of the actual water consumption of users is not high. Contents of the invention [0003] Provided are a water consumption prediction method, device,...

AI Technical Summary

Problems solved by technology

[0002] The water consumption forecast in the daily life of the water affairs group generally uses network models, such as ARIMA (Autoregressive Integrated Moving Average Model, autoregressive moving average model), RNN (Recurrent Neural Network, cyclic neural network), etc. to predict water sales. The features input to the model include internal features and external features. Internal features include store location, latitude and longitude, etc. External features include promotional volume, etc. The accuracy of the user's actual water consumption prediction is not high

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