Exploratory Data Analysis for Building Energy Meters Using Machine Learning
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Abstract
The purpose of this research was to apply exploratory data analysis techniques to building energy meters, such as electricity, cold, heat, and steam meters. A thorough understanding of energy usage patterns becomes increasingly vital in an era of growing awareness of energy management and sustainability. Trends, patterns, and anomalies can be identified in building energy meter data using meticulous data exploration approaches, which can give significant insights for increasing energy efficiency. Exploratory data analysis combined with machine learning approaches may be was used to reveal hidden patterns of energy usage and examine the links between relevant factors. The findings of this exploratory data analysis gave vital insights into building energy use trends. Some significant and hidden information that was crucial for understanding energy usage within a certain time frame in each building was discovered via the investigation of the data used in this study. Steam had the highest use, whereas electricity had the lowest. Utilities were more popular before 5 a.m., followed by healthcare, with daytime use hours beginning around 10 a.m., depending on the area. During the working day, the industry needs more energy. Places of worships use more energy on weekends. There was a significant relation between the number of floors and spaces per level of a building and the height meter reading between May and October. There is a significant association between the kind of buildings used for schools, workplaces and high energy use. This study significantly contributed to the management of the energy and sustainability domains. Using exploratory data analysis and machine learning approaches to building energy meters could optimize energy usage, minimize running costs, and enhance overall energy efficiency. This research is still very open to be continued using other methods, to obtain other hidden information.
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