Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Energy efficiency in buildings requires having good predic- tion of the variables that define the por consumption in the building. Temperature is the most relevant of these variables because it affects the operation of the cooling systems in summer and the heating systems in win- ter, while being also the main variable that defines comfort.  presents the application of classical methods of time series forecasting, such as Autoregressive (AR), Multiple Linear Regression (MLR) and Robust MLR (RMLR) models, along with others derived from more complex ma- chine learning techniques, including Multilayer Perceptron with Non-linear Autoregressive Exogenous (MLP-NARX) and Extreme Learning Machine (ELM), to forecast temperature in buildings. The results obtained in the temperature prediction of several rooms of a building show the goodness of machine learning methods as compared to traditional approaches.
['Fernando Mateo', 'Juan José Carrasco', 'Mónica Millán-Giraldo', 'Abderrahim Sellami', 'Pablo Escandell-Montero', 'José María Martínez-Martínez', 'Emilio Soria-Olivas']
I am writing a research paper on a idea Temperature Forecast in Buildings Using Machine Learning Techniques.
[{"from": "human", "value": "I am writing a research paper on a idea Temperature Forecast in Buildings Using Machine Learning Techniques."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Energy efficiency in buildings requires having good predic- tion of the variables that define the por consumption in the building. Temperature is the most relevant of these variables because it affects the operation of the cooling systems in summer and the heating systems in win- ter, while being also the main variable that defines comfort.  presents the application of classical methods of time series forecasting, such as Autoregressive (AR), Multiple Linear Regression (MLR) and Robust MLR (RMLR) models, along with others derived from more complex ma- chine learning techniques, including Multilayer Perceptron with Non-linear Autoregressive Exogenous (MLP-NARX) and Extreme Learning Machine (ELM), to forecast temperature in buildings. The results obtained in the temperature prediction of several rooms of a building show the goodness of machine learning methods as compared to traditional approaches."}]