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- W3127785284 abstract "The growing trend within corporate and residential building structures in India is moving towards self-sustainable techniques like intelligent temperature control, usage of renewable sources of energy and efficient load forecasting. Out of the multiple factors governing the smart buildings, controlled indoor room temperature is considered important, as it drives the need of cooling or heating through air conditioners or heat pumps. Increase in need of self sustainability of building structures and considering Indian climatic and geographical conditions, existing literature suggests that there is a demand to contribute further in this area. This paper explores the application of efficient computational data analysis techniques like Machine Learning to linear time series forecasting methods. It presents the results of a pilot study conducted to perform systematic indoor room temperature forecasting of a corporate building structure, considering Indian climatic and geographical conditions in mind. Out of three models selected for the study, the results show that Multiple Exogenous Inputs fed Auto-Regressive Integrated Moving Average model is the best fit model." @default.
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- W3127785284 date "2018-01-01" @default.
- W3127785284 modified "2023-09-23" @default.
- W3127785284 title "Systematic Indoor Room Temperature Forecasting for Smart Buildings Using Machine Learning" @default.
- W3127785284 doi "https://doi.org/10.2139/ssrn.3170744" @default.
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