Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach
Manuel Peña1, Félix Biscarri1, Enrique Personal1
1Electronic Technology Department, School of Computer Science and Engineering, University of Seville, Av. Reina Mercedes S/N, 41012 Seville, Spain.
Sensors (Basel, Switzerland)
|February 26, 2022
Summary
This study introduces an intelligent data analysis method for smart building energy efficiency. It uses Data Analytics (DA) to create a Decision Support System (DSS) for optimizing energy use and detecting anomalies.
Area of Science:
- Building Energy Management
- Data Science Applications
- Smart Building Technology
Background:
- Smart buildings generate vast amounts of data crucial for operational efficiency.
- Optimizing energy efficiency in buildings is a key challenge for sustainability and cost reduction.
- Existing methods may lack the sophistication to identify subtle patterns and anomalies in energy consumption.
Purpose of the Study:
- To propose an intelligent data analysis method for modeling and optimizing energy efficiency in smart buildings.
- To develop a Decision Support System (DSS) for experts to quantify and enhance building energy efficiency.
- To enable early detection of anomalous behaviors impacting energy consumption.
Main Methods:
- Utilizing Data Analytics (DA) on historical building data and Energy Efficiency Indicators (EEIs).
- Extracting knowledge from behavioral patterns to develop a classification method for diverse daily features and seasons.
- Analyzing clusters to infer key features for predicting and quantifying energy efficiency.
Main Results:
- The method successfully analyzed historical data to identify behavioral patterns.
- A classification approach was developed to compare and group days based on various characteristics.
- Key features were inferred to predict energy efficiency for similar but potentially different daily behaviors.
- Insights were revealed highlighting inefficiencies and correlating anomalous behaviors with energy efficiency (EE).
Conclusions:
- The proposed intelligent data analysis method provides valuable insights for smart building energy management.
- The Decision Support System (DSS) aids experts in optimizing energy efficiency and detecting anomalies.
- The approach demonstrated effectiveness on the BlueNet building and integration with commercial tools like Eugene.
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