Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent advances in smart metering technology enable utility companies to have access to tremendous amount of smart meter data, from which the utility companies are eager to gain more insight about their customers. ,  aim to detect electric heat pumps from coarse grained smart meter data for a heat pump marketing campaign. Hover, appliance detection is a challenging task, especially given a very low granularity and partial labeled even unlabeled data. Traditional methods install either a high granularity smart meter or sensors at every appliance, which is either too expensive or requires technical expertise.  propose a novel approach to detect heat pumps that utilizes low granularity smart meter data, prior sales data and ather data. In particular, motivated by the characteristics of heat pump consumption pattern,  extract novel features that are highly relevant to heat pump usage from smart meter data and ather data. Under the constraint that only a subset of heat pump users are available,  formalize the problem into a positive and unlabeled data classification and apply biased Support Vector Machine (BSVM) to our extracted features. Our empirical study on a real-world data set demonstrates the effectiveness of our method. Furthermore, our method has been deployed in a real-life setting where the partner electric company runs a targeted campaign for 292,496 customers. Based on the initial feedback, our detection algorithm can successfully detect substantial number of non-heat pump users who re identified heat pump users with the prior algorithm the company had used.
['Hongliang Fei', 'Younghun Kim', 'Sambit Sahu', 'Milind R. Naphade', 'Sanjay K. Mamidipalli', 'John Hutchinson']
I am writing a research paper on a idea Heat pump detection from coarse grained smart meter data with positive and unlabeled learning
[{"from": "human", "value": "I am writing a research paper on a idea Heat pump detection from coarse grained smart meter data with positive and unlabeled learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent advances in smart metering technology enable utility companies to have access to tremendous amount of smart meter data, from which the utility companies are eager to gain more insight about their customers. ,  aim to detect electric heat pumps from coarse grained smart meter data for a heat pump marketing campaign. Hover, appliance detection is a challenging task, especially given a very low granularity and partial labeled even unlabeled data. Traditional methods install either a high granularity smart meter or sensors at every appliance, which is either too expensive or requires technical expertise.  propose a novel approach to detect heat pumps that utilizes low granularity smart meter data, prior sales data and ather data. In particular, motivated by the characteristics of heat pump consumption pattern,  extract novel features that are highly relevant to heat pump usage from smart meter data and ather data. Under the constraint that only a subset of heat pump users are available,  formalize the problem into a positive and unlabeled data classification and apply biased Support Vector Machine (BSVM) to our extracted features. Our empirical study on a real-world data set demonstrates the effectiveness of our method. Furthermore, our method has been deployed in a real-life setting where the partner electric company runs a targeted campaign for 292,496 customers. Based on the initial feedback, our detection algorithm can successfully detect substantial number of non-heat pump users who re identified heat pump users with the prior algorithm the company had used."}]