Towards Buildings Energy Management: Using Seasonal Schedules Under Time of Use Pricing Tariff via Deep Neuro-Fuzzy Optimizer

Management of increasing amount of the electricity information provided by the smart meters is becoming more valuable and a very challenging issue in modern era, especially in residential sector for maintaining the records of consumers' consumption patterns. It becomes the necessity of retailers and utilities to provide the consumers more effective demand response programs for handling the uncertainties of their consumption patterns. In order to deal with the unceratian behaviours of the consumers and their unprecedented high volume of data, this work introduces the deep neuro-fuzzy optimizer for effective load and cost optimization. Three premises parameters: energy consumption, price and time of the day and two consequents parameters: peak and cost reduction are used for the opti-mization process of the optimizer. The dataset is taken from the Pecan Street Incorporation site and Takagi Sugeno fuzzy inference system is used for the evaluation of the rules developed from the memebership functions of the parameters. Membership Functions (MFs) are chosen as Guassian MFs for continuously monitoring the consumers' behaviours. Performance of this proposed energy optimizer is validated through the simulations which shows the robustness of optimizer in cost optimization and energy efficiency.