Version 1
: Received: 22 July 2024 / Approved: 23 July 2024 / Online: 23 July 2024 (09:52:05 CEST)
How to cite:
Singh, N. K.; Nagahara, M. SHAP, LightGBM, and Correlation Matrix Based Framework for Analyzing Household Energy Data: Towards Electricity Self-Sufficiency. Preprints2024, 2024071769. https://doi.org/10.20944/preprints202407.1769.v1
Singh, N. K.; Nagahara, M. SHAP, LightGBM, and Correlation Matrix Based Framework for Analyzing Household Energy Data: Towards Electricity Self-Sufficiency. Preprints 2024, 2024071769. https://doi.org/10.20944/preprints202407.1769.v1
Singh, N. K.; Nagahara, M. SHAP, LightGBM, and Correlation Matrix Based Framework for Analyzing Household Energy Data: Towards Electricity Self-Sufficiency. Preprints2024, 2024071769. https://doi.org/10.20944/preprints202407.1769.v1
APA Style
Singh, N. K., & Nagahara, M. (2024). SHAP, LightGBM, and Correlation Matrix Based Framework for Analyzing Household Energy Data: Towards Electricity Self-Sufficiency. Preprints. https://doi.org/10.20944/preprints202407.1769.v1
Chicago/Turabian Style
Singh, N. K. and Masaaki Nagahara. 2024 "SHAP, LightGBM, and Correlation Matrix Based Framework for Analyzing Household Energy Data: Towards Electricity Self-Sufficiency" Preprints. https://doi.org/10.20944/preprints202407.1769.v1
Abstract
This article aims to analyze household energy data to predict electricity self-sufficiency and identify the key features that impact it. For this purpose, we use SHAP (Shapley Additive Explanations), LightGBM (Light Gradient Boosting Machine), and a correlation heatmap-based framework to analyze 12 months of energy and questionnaire survey data collected from over 200 smart houses in Kitakyushu, Japan. We use the SHAP summary plot to identify the impact-wise order of key features influencing the electricity self-sufficiency rate (ESSR). Using SHAP, we demonstrated that key features are; housing types, average monthly electricity bill, floor heating, electric capacity, number of washing and drying machines, fee plans, occupation of household head, total floor area, average monthly gas bill, cooking equipment, etc. Furthermore, we adopted the LightGBM classifier with $\ell^1$ regularization to extract the most significant features and predict the electricity self-sufficiency rate of households. This LightGBM-based model can also predict the electricity self-sufficiency rate of households that did not participate in the questionnaire survey. A heat map is also used to analyze the correlation among household variables. The findings discussed in this article offer valuable insights for energy policymakers to achieve the targets of energy self-sufficient houses.
Keywords
SHAP; LightGBM; $\ell^1$ regularization; electricity self-sufficiency rate; household energy data; time-series data; questionnaire survey; renewable energy sources; Net zero-energy houses; correlation matrix; energy policies; smart houses; HEMS
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
