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It could also be helpful on the supply side for planning electricity demand for a specific household. A model that makes use of multiple input variables may be referred to as a multivariate multi-step time series forecasting model.Ī model of this type could be helpful within the household in planning expenditures. Technically, this framing of the problem is referred to as a multi-step time series forecasting problem, given the multiple forecast steps. This requires that a predictive model forecast the total active power for each day over the next seven days. Given recent power consumption, what is the expected power consumption for the week ahead? In this tutorial, we will use the data to explore a very specific question that is: There are many ways to harness and explore the household power consumption dataset. This section is divided into four parts they are: In this section, we will consider how we can develop and evaluate predictive models for the household power dataset.
The data was collected between December 2006 and November 2010 and observations of power consumption within the household were collected every minute. How to Load and Explore Household Electricity Usage Data.The ‘ Household Power Consumption‘ dataset is a multivariate time series dataset that describes the electricity consumption for a single household over four years.įor more about this dataset, see the post: How to Setup Amazon AWS EC2 GPUs to Train Keras Deep Learning Models.How to Setup a Python Environment for Machine Learning and Deep LearningĪ GPU is not required for this tutorial, nevertheless, you can access GPUs cheaply on Amazon Web Services.If you need help with your environment, see this tutorial: The tutorial also assumes you have scikit-learn, Pandas, NumPy, and Matplotlib installed. You must have Keras (2.2 or higher) installed with either the TensorFlow or Theano backend. This tutorial assumes you have a Python SciPy environment installed, ideally with Python 3. ConvLSTM Encoder-Decoder Model With Univariate Input.CNN-LSTM Encoder-Decoder Model With Univariate Input.Encoder-Decoder LSTM Model With Multivariate Input.Encoder-Decoder LSTM Model With Univariate Input.LSTM Model With Univariate Input and Vector Output.This tutorial is divided into nine parts they are: Photo by Ian Muttoo, some rights reserved. How to Develop LSTM Models for Multi-Step Time Series Forecasting of Household Power 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. 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. 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.