Pages that link to "Item:Q1722287"
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The following pages link to An optimized forecasting approach based on grey theory and cuckoo search algorithm: a case study for electricity consumption in New South Wales (Q1722287):
Displaying 9 items.
- An integrated fuzzy regression algorithm for improved electricity consumption estimation (Q604788) (← links)
- Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption (Q884650) (← links)
- A novel hybrid FA-based LSSVR learning paradigm for hydropower consumption forecasting (Q905150) (← links)
- Short-term wind speed forecasting using support vector regression optimized by cuckoo optimization algorithm (Q1666155) (← links)
- Annual energy consumption forecasting based on PSOCA-GRNN model (Q1722367) (← links)
- Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting (Q1792298) (← links)
- Data analysis-based time series forecast for managing household electricity consumption (Q2680559) (← links)
- (Q4783484) (← links)
- APPLICATION OF OPTIMIZED FRACTIONAL GREY MODEL-BASED VARIABLE BACKGROUND VALUE TO PREDICT ELECTRICITY CONSUMPTION (Q5024765) (← links)
