The statistical Modelling of Residential Electrical Demand for the Evaluation of Impacts that Result from Demand Side Management Interventions

Inhalt:
Sustainable capital investments in Demand Side Management and Energy Efficiency projects require accurate measurement and verification of the savings impacts. Assessments based on purely deterministic baseline models do not adequately represent the statistical nature of the savings impacts of many practical load systems. This paper describes a generic probabilistic methodology for determining adjusted baseline profiles for such applications. The difference between the predicted model demand and the recorded demand for a particular set of variables represent the electrical demand impact. The baseline model is defined in terms of Probability Density Functions of the energy consumption profile data, which are derived using a multivariate kernel density estimation algorithm. The approach is tested using a simulation model of a water-heating geyser implemented in MATLAB, for which a statistical energy consumption profile baseline model is derived using the simulated electrical demand data obtained from the hot water heater model. The choice of method to generate the statistical model from the input/output data and the methodology to evaluate the suitability of the model to represent the data are subsequently discussed. This case study is of particular importance for assessing the savings impacts of heat pump technologies and solar water heaters.