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About Household-Level Energy Forecasting in Smart Buildings Using A Novel Hybrid Deep Learning Model
This document presents a novel hybrid deep learning model for household-level energy forecasting in smart buildings. The proposed model uses a two-stage approach: 1) data cleaning and 2) model building. In the model building stage, the hybrid model stacks fully connected layers, unidirectional LSTMs, and bidirectional LSTMs to capture temporal dependencies in energy consumption data. The model achieves superior forecasting accuracy compared to other hybrid deep learning models, with a mean absolute percentage error of 2.00-3.71% on two energy consumption datasets. The model also improves multi-step week-ahead forecasting accuracy by 8.37-20.99% compared to an LSTM-based model.
Detailed Information
| Author: | ['Winnera'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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