Energy Consumption Prediction using Data Stream Learning for Commercial Buildings

Smart cities should be built on harmonious relationships with the environment, with rational measures to use environmental resources in many ways. Resources essential to human life such as water and energy must have special attention. With the advance of technologies use, human being becomes one of the main promoters of information and solutions for smart cities. Stream machine learning is an approach where data becomes available sequentially, called data stream, which adapts the prediction model with fixed window-sized data stream. The objective of this work is to analyze the effectiveness of buildings energy consumption using stream learning. To achieve this goal, MOA
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Massive Online Analysis tool was used to evaluate the performance of the selected stream learning algorithms on an an open database. Batch algorithms were also used as testbed, to analyze the performance of stream algorithms related to them. Our work shows that stream learning can show good results in this scenario.