FORECASTING CONSUMER ACTIVITY USING MACHINE LEARNING METHODS

Abstract

This article discusses forecasting consumer activity, and in particular forecasting household energy consumption using machine learning. Forecasting household energy consumption using machine learning is a topic that addresses various aspects of efficient and environmentally friendly use of electricity. The article discusses various machine learning methods and models that can be applied to solve the forecasting problem. The consideration of a neural network model such as LTSM is highlighted in a separate category, its description, the learning and use process are given, as well as the advantages and disadvantages of this model are given. After that, a model is trained on the prepared dataset to predict energy consumption.

Keywords

• energy consumption
• data processing
• machine learning
• forecasting
• neural networks
• deep learning