Modelo causal del consumo energético residencial para pronosticar la demanda

Abstract

Energy consumption forecast is necessary for planning and expansion (short and long term) inside electrical systems. Today, exists a lot of methodologies related to estimate energy consumption (temporal series handling, statistic methods, regression methods, econometric methods, neural network methods, etc.) which them analyze this phenomenon since a general perspective. However, recently researches using dynamic model DM concept, studying demand behavior under specifically causality conditions (random values, externally, etc.). Maybe them have been a little or neither researched, them give the possibility to create simulation models with capable to interact witch external variables which affect the electricity consumption (dynamic behavior). This paper, details typically household devices, consumption hours and probability distribution functions, to simulate over VENSIM a dynamic model, based on casual diagrams (schemes cause and effect), for forecast tasks. The model could be used to compute an approximate estimation for a specific population sample from 𝑛 houses with similar features-conditions.