Identifying Energy Inefficiencies Using Self-Organizing Maps: Case of A Highly Efficient Certified Office Building

Abstract

Living and working in comfort while a building’s energy consumption is kept under control requires monitoring a system’s consumption to optimize the energy performance. The way energy is generally used is often far from optimal, which requires the use of smart meters that can record the energy consumption and communicate the information to an energy manager who can analyze the consumption behavior, monitor, and optimize energy performance. Given that the heating, ventilation, and air conditioning (HVAC) systems are the largest electricity consumers in buildings, this paper discusses the importance of incorporating occupancy data in the energy efficiency analysis and unveils energy inefficiencies in the way the system operates. This paper uses 1-year data of a highly efficient certified office building located in the Houston area and shows the power of self-organizing maps and data analysis in identifying up to 4.6% possible savings in energy. The use of time series analysis and machine-learning techniques is conducive to helping energy managers discover more energy savings.

A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical WSN Using Spatial Self-Organizing Feature Map

“…On the other hand, the work in ref. [22] used the self-organizing map technique for high buildings, in the HVAC system, to find energy-saving opportunities and thus efficiently distribute the energy. This work used a dataset spanning one-year in time series analysis to provide a service that accommodates user requests regarding outdoor/indoor temperature.…”

“…Electronics 2024, 13, x FOR PEER REVIEW 7 of 14 22. Assign the node with the minimum distance as the feature for the sensor node 23.…”