System for predicting electricity consumption in food production based on streaming data

Abstract

System for predicting electricity consumption in food production based on streaming data

Abuzyarov A.A., Makarov A.A.

Incoming article date: 15.11.2022

The purpose of this work is to implement a system for predicting electricity consumption in food production and to select the most appropriate method for training the forecasting model. In this work, a system was implemented for predicting electricity consumption based on streaming data, which receives them in "real time". The system is implemented on the principle of microservice architecture, where the following were implemented: a service for collecting data from meters, a service for data aggregation and forecasting services. Two forecasting services were implemented: using the classical learning approach based on the ARIMA model and the online learning approach using the HATR online model, the results of which were compared using tests for predicting anomalous values and forecasting under conditions of a change in the data concept, or drift concepts.

Keywords: machine learning, online learning, online model, concept drift, data drift