A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods
Mahziyar Dostmohammadi, Mona Zamani Pedram, Siamak Hoseinzadeh, Davide Astiaso Garcia
Abstract
scores were 0.983, 0.985, and 0.999, respectively. This represents a substantial advancement in forecasting the energy consumption of residential buildings. Such progress underscores the potential advantages of integrating this framework into the practices of building designers, thereby fostering informed decision-making, design management, and optimization prior to construction.
Topics & Concepts
StackingEnergy consumptionEnsemble learningEnsemble forecastingComputer scienceConsumption (sociology)EconometricsArtificial intelligenceEngineeringEconomicsChemistrySociologySocial scienceOrganic chemistryElectrical engineeringEnergy Load and Power ForecastingBuilding Energy and Comfort OptimizationSmart Grid Energy Management