Algorithms for clustering data
Algorithms for clustering data
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Multiple fuzzy model-based temperature predictive control for HVAC systems
Information Sciences: an International Journal
Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system
Expert Systems with Applications: An International Journal
Boosting k-nearest neighbor classifier by means of input space projection
Expert Systems with Applications: An International Journal
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Prediction of building energy needs in early stage of design by using ANFIS
Expert Systems with Applications: An International Journal
K nearest neighbor reinforced expectation maximization method
Expert Systems with Applications: An International Journal
Smart meter monitoring and data mining techniques for predicting refrigeration system performance
Expert Systems with Applications: An International Journal
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This paper addresses the problem of predicting demand for natural gas for the purpose of realizing energy cost savings. Daily monitoring of a rooftop unit wireless sensor system provided feedback for a decision support system that supplied the demand for the required number of million cubic feet of natural gas used to control heating, ventilation, and air conditioning systems. The system was modeled with artificial neural networks (ANNs). Data on the consumption of the system were collected for 111days beginning September 21, 2012. The input/output data were used to train the ANN. The ANN approximated the data very well, showing that it can be used to predict demand for natural gas. A fuzzy nearest neighbor neural network statistical model consisting of four components was used. The predictive models were implemented by comparing regression, fuzzy logic, nearest neighbor, and neural networks. In addition, to optimize natural gas demand, we used the fuzzy regression nearest neighbor ANN model cost function to investigate the variables of price, operating expenses, cost to drill new wells, cost to turn gas on, oil price and royalties.