Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Currently many commercial buildings are not continuously monitored for energy consumption, especially small buildings which constitute 90% of all such buildings. Hover, readily available data from the electric meters can be used for monitoring and analyzing energy consumption. Efficient utilization of available historical data (from these meters) can potentially improve energy efficiency, help to identify common energy wasting problems, and, in the future, enable various Smart Grid programs, such as demand response, real-time pricing etc.  describes application of computational intelligence techniques for prediction of electric por consumption. The proposed approach combines regression and clustering methods, in order to improve the prediction accuracy of por consumption, as a function of time (of the day) and temperature, using real-life data from several commercial and government buildings. Empirical comparisons show that the proposed approach provides an improvement over the currently used bin-based method for modeling por consumption.
['Vladimir Cherkassky', 'Sohini Roy Chowdhury', 'Volker Landenberger', 'Saurabh Tewari', 'Paul Bursch']
I am writing a research paper on a idea Prediction of electric power consumption for commercial buildings
[{"from": "human", "value": "I am writing a research paper on a idea Prediction of electric power consumption for commercial buildings"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Currently many commercial buildings are not continuously monitored for energy consumption, especially small buildings which constitute 90% of all such buildings. Hover, readily available data from the electric meters can be used for monitoring and analyzing energy consumption. Efficient utilization of available historical data (from these meters) can potentially improve energy efficiency, help to identify common energy wasting problems, and, in the future, enable various Smart Grid programs, such as demand response, real-time pricing etc.  describes application of computational intelligence techniques for prediction of electric por consumption. The proposed approach combines regression and clustering methods, in order to improve the prediction accuracy of por consumption, as a function of time (of the day) and temperature, using real-life data from several commercial and government buildings. Empirical comparisons show that the proposed approach provides an improvement over the currently used bin-based method for modeling por consumption."}]