Abstract
As we know, whole world relies on corporate function, whether it is a house flat or any corporate office, its infrastructure should be technically smart. Corporate functions are handling a huge amount of data like data related to lighting, air conditioning, security alarms etc. So, manual process will take too much of time to find faults and take necessary actions for the same. Data mining technique is used in this invention to predict the system’s behavior and their outcomes. This technology is also efficient for handling the record’s history and also the number of parameters required in any premises. By regression-based methods, energy consumption is predicted after retrieving information from all sensors. Now, all the data which is retrieved will be converted into a particular format required, and then, desired features from that format will be selected. Now, optimization of the model parameters will be done, and then prediction will be done by using machine learning algorithm. Patter which will be obtained is further recognized by k-means clustering method. After prediction and identification, fault will be recognized and a quick action will be performed to overcome the fault. We implemented our methodology using latest features of R Programming.
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Sharma, N., Vargis, B.K., Upreti, K., Jain, R., Sharma, A.K. (2022). Energy Configuration Management Framework Using Automated Data Mining Algorithm. In: Mohanty, M.N., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 430. Springer, Singapore. https://doi.org/10.1007/978-981-19-0825-5_8
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