Multi-Step LSTM Time Series Forecasting Models for Power Usage
Last Updated on August 28, 2020
Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available.
This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption.
Unlike other machine learning algorithms, long short-term memory recurrent neural networks are capable of automatically learning features from sequence data, support multiple-variate data, and can output a variable length sequences that can be used for multi-step forecasting.
In this tutorial, you will discover how to develop long short-term memory recurrent neural networks for multi-step time series forecasting of household power consumption.
After completing this tutorial, you will know:
- How to develop and evaluate Univariate and multivariate Encoder-Decoder LSTMs for multi-step time series forecasting.
- How to develop and evaluate an CNN-LSTM Encoder-Decoder model for multi-step time series forecasting.
- How to develop and evaluate a ConvLSTM Encoder-Decoder model for multi-step time series forecasting.
Kick-start your project with my new book Deep Learning for Time Series Forecasting, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.