Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a supervised classification model, where the indicators of correlation beten dependent and independent variables within each class are utilized for a transformation of the large-scale input data to a lor dimension without loss of recognition relevant information. In the case study,  use the consumption data recorded by smart electricity meters of 4200 Irish dllings along with half-hourly outdoor temperature to derive 12 household properties (such as type of heating, floor area, age of house, number of inhabitants, etc.). Survey data containing characteristics of 3500 households enables algorithm training. The results show that the presented model outperforms ordinary classifiers with regard to the accuracy and temporal characteristics. The model allows incorporating any kind of data affecting energy consumption time series, or in a more general case, the data affecting class-dependent variable, while minimizing the risk of the curse of dimensionality. The gained information on household characteristics renders targeted energy-efficiency measures of utility companies and public bodies possible.
['Mariya A. Sodenkamp', 'Ilya Kozlovskiy', 'Thorsten Staake']
I am writing a research paper on a idea Supervised classification with interdependent variables to support targeted energy efficiency measures in the residential sector
[{"from": "human", "value": "I am writing a research paper on a idea Supervised classification with interdependent variables to support targeted energy efficiency measures in the residential sector"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a supervised classification model, where the indicators of correlation beten dependent and independent variables within each class are utilized for a transformation of the large-scale input data to a lor dimension without loss of recognition relevant information. In the case study,  use the consumption data recorded by smart electricity meters of 4200 Irish dllings along with half-hourly outdoor temperature to derive 12 household properties (such as type of heating, floor area, age of house, number of inhabitants, etc.). Survey data containing characteristics of 3500 households enables algorithm training. The results show that the presented model outperforms ordinary classifiers with regard to the accuracy and temporal characteristics. The model allows incorporating any kind of data affecting energy consumption time series, or in a more general case, the data affecting class-dependent variable, while minimizing the risk of the curse of dimensionality. The gained information on household characteristics renders targeted energy-efficiency measures of utility companies and public bodies possible."}]