The U.S. energy mix is highly weighted toward fossil fuels and concerns about fossil fuel reliance have increased pressure on policy-makers to reduce this dependence. Energy-saving and advanced technologies, such as renewable-energy based systems, Plug-in Electric Vehicles (PEVs), energy storage devices, controllable appliances and distributed cogeneration are often suggested as parts of the key to face this crisis. Possible advantages in terms of energy and cost savings with respect to integrated energy systems and household management can be investigated by modelling the interactions between different sub-system components, namely power grid, household power demand prediction, renewable energy source, energy storage unit and PEV. This paper presents a model to simulate the power demand of a single household consisting of multiple individuals. Activity patterns for the individuals are modelled using a heterogeneous Markov chain, and the total power consumption of the household is computed based on the activity patterns, lighting and cold appliances consumption. Using data collected by the U.S. Census Bureau, a case study for typical U.S. consumers has been developed. The data have been used to conduct an in-sample validation of the modelled activities. The results show highly realistic patterns and capture annual and diurnal variations, load fluctuations, and diversity between households, location, and different household sizes. The model developed can serve as a tool to evaluate the impact of different energy technologies, such as low-power appliances, automated appliance or domestic control systems, and assess population behaviour and predisposition toward energy saving.

Residential Power Demand Prediction and Modelling

MARANO, VINCENZO;
2011-01-01

Abstract

The U.S. energy mix is highly weighted toward fossil fuels and concerns about fossil fuel reliance have increased pressure on policy-makers to reduce this dependence. Energy-saving and advanced technologies, such as renewable-energy based systems, Plug-in Electric Vehicles (PEVs), energy storage devices, controllable appliances and distributed cogeneration are often suggested as parts of the key to face this crisis. Possible advantages in terms of energy and cost savings with respect to integrated energy systems and household management can be investigated by modelling the interactions between different sub-system components, namely power grid, household power demand prediction, renewable energy source, energy storage unit and PEV. This paper presents a model to simulate the power demand of a single household consisting of multiple individuals. Activity patterns for the individuals are modelled using a heterogeneous Markov chain, and the total power consumption of the household is computed based on the activity patterns, lighting and cold appliances consumption. Using data collected by the U.S. Census Bureau, a case study for typical U.S. consumers has been developed. The data have been used to conduct an in-sample validation of the modelled activities. The results show highly realistic patterns and capture annual and diurnal variations, load fluctuations, and diversity between households, location, and different household sizes. The model developed can serve as a tool to evaluate the impact of different energy technologies, such as low-power appliances, automated appliance or domestic control systems, and assess population behaviour and predisposition toward energy saving.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/3879688
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