A model of electric residential end-use is proposed for establishing the load diagram of an area by a process of synthesis. The model follows a `bottom-up' approach, allowing construction of the relative load shape of the area, starting from knowledge of its most relevant socioeconomic and demographic characteristics, Unitary Energy Consumption (UEC) and the load profiles of individual household appliances. Several probability functions have been introduced in order to cover the close relationship existing between the demand of residential customers and the psychological and behavioral factors typical of the household; the model makes frequent use of the latter through a Monte-Carlo extraction process. The model has been applied for the simulation of a residential area where field measurements of power demand had been made at 15-minute intervals and a combined-mail survey had been conducted to investigate household energy usage. The paper reports the results of a comparison between recorded and predicted load profiles
The paper illustrates a part of the research activity conducted by authors in the field of electric Short Term Load Forecasting (STLF) based on Artificial Neural Network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architectures provide results comparable with those obtained by human operators for most normal days, they evidence some accuracy deficiencies when applied to ''anomalous'' load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen's Self Organizing Map (SOM) The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer perceptron with a backpropagation learning algorithm similar to the ones above mentioned. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.