# Multistep Multiappliance Load Prediction

arXiv: https://arxiv.org/abs/2212.09426 · HTML: https://mlsanity.com/paper/2212.09426/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration](https://mlsanity.com/paper/2004.02551/) (2020)
- [Multi-Step-Ahead Time Series Prediction using Multiple-Output Support Vector Regression](https://mlsanity.com/paper/1401.2504/) (2014)
- [VEST: Automatic Feature Engineering for Forecasting](https://mlsanity.com/paper/2010.07137/) (2020)
- [Short-Term Load Forecasting for Smart HomeAppliances with Sequence to Sequence Learning](https://mlsanity.com/paper/2106.15348/) (2021)
- [Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning](https://mlsanity.com/paper/2109.12440/) (2021)
- [Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets](https://mlsanity.com/paper/2011.14137/) (2020)

## Abstract (arXiv)

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.