Energy Efficient Energy Analytics
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Smart meters allow for hourly data collection related to customer's power consumption. However this results in thousands of data points, which hides broader trends in power consumption and makes it difficult for energy suppliers to make decisions regards to a specific customer or to large number of customers. Since data without analysis is useless, various algorithms have been proposed to lower the dimensionality of data, discover trends (eg. regression), study relationships between different types (eg. temperature and power data) of collected data, summarize data (e.g. histogram). This allows for easy consumption by the end user. The smart meter data is very compute intensive to process as there are a large number of houses and each house has the data collected over a few years. To speed up the smart meter data analysis, computer clusters have been used. Ironically, these clusters consume a lot of power. Studies have shown that about 10 % of power is consumed by the computing infrastructure. In this thesis a GPU will be used to perform analysis of smart meter data and it will be compared to a baseline CPU implementation. It will also show that GPUs are not only faster than the CPU, but they are also more power efficient.
Cite this work
Sagnik De (2017). Energy Efficient Energy Analytics. UWSpace. http://hdl.handle.net/10012/11928