Multistep Multiappliance Load Prediction

TL;DR

This paper introduces a three-step framework for multiappliance load prediction, combining predictability analysis, feature engineering, and deep learning to improve accuracy and robustness across diverse datasets.

Contribution

It presents a novel multi-step approach integrating predictability assessment, new feature design, and deep learning models for appliance-level load forecasting.

Findings

Cyclical encoding of time and weather features improves prediction accuracy.

LSTM models outperform other deep learning and benchmark methods.

The framework is validated across four international datasets.

Abstract

A well-performing prediction model is vital for a recommendation system suggesting actions for energy-efficient consumer behavior. However, reliable and accurate predictions depend on informative features and a suitable model design to perform well and robustly across different households and appliances. Moreover, customers' unjustifiably high expectations of accurate predictions may discourage them from using the system in the long term. In this paper, we design a three-step forecasting framework to assess predictability, engineering features, and deep learning architectures to forecast 24 hourly load values. First, our predictability analysis provides a tool for expectation management to cushion customers' anticipations. Second, we design several new weather-, time- and appliance-related parameters for the modeling procedure and test their contribution to the model's prediction performance. Third, we examine six deep learning techniques and compare them to tree- and support vector regression benchmarks. We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions (US, UK, Austria, and Canada) with an equal set of appliances. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.