Short-Term Energy Forecasting Using an Ensemble Deep Learning Approach

DOI

10.2991/978-94-6463-471-6_32How to use a DOI?

Keywords

Energy Management; Electric power Consumption; Computational Viability; Machine learning LSTM networks; smart sensor systems

Abstract

Precise estimation of domestic electricity usage is essential for sustainable energy management, enabling effective energy allocation, and advancing the development of intelligent networks. To anticipate home electric power consumption from time series data, this research investigates usage of advanced machine learning models, including Recurrent neural networks. The dataset includes observations of a household's electricity use over a long period, together with details like usage patterns and time of day. To address anomalies, standardize the series, and organize the data for sequential learning, we preprocess it. The study assesses the performance of each model, finding that GRUs are better at spotting spatial-temporal patterns in the data, RNNs are better at sequential data prediction, and LSTMs are better at capturing long-term dependencies. To increase prediction accuracy, the comparison study lays the groundwork for future efforts to optimize model architectures and incorporate outside variables like weather and economic data. This study highlights how deep learning has the ability to change energy management procedures and open the door to more economical and environmentally friendly home energy use.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - P. Yogendra Prasad
AU  - M. Ramu
AU  - Annavarapu Yasaswini
AU  - Mallela Gowthami
AU  - Putta Sai Harika
AU  - Chettipalli Abhishek
PY  - 2024
DA  - 2024/07/30
TI  - Short-Term Energy Forecasting Using an Ensemble Deep Learning Approach
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 325
EP  - 333
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_32
DO  - 10.2991/978-94-6463-471-6_32
ID  - Prasad2024
ER  -