Predicción con series de tiempo para la optimización de la demanda eléctrica residencial

This draft develops a modeling allows to predict the behavior of annual electricity demand residential, is to say to forecast values to take in certain times. In this investigation draft alone is considered to residential level, having as need to minimize costs of energy electric consumption to that be representative for the user, for the reason that currently reading of electromechanical and electronic counters are done manually and usually monthly, to determine the energy billing. The development of the investigation is carried out through time 2 series, where it be identify that electric demand is not static, because varies through time by so that was chosen the method of static prognosis for time series, where is determined the main components of a time series. For the modeling of residential demand it was required determine a linear regression with on data from the electricity consumption of historical data from 2013 provided by Empresa Eléctrica Quito, to later to make the prediction for 2016, scheduled and presented in the Matlab tool informatic, showing the prediction of individual and total demand. For optimization it is manually developed considering a referential consumption limit heuristically or by the simplex method to minimize a function, to simulate the reduction in peak response curve of the forecasted demand.El presente proyecto desarrolla un modelamiento que permita predecir el comportamiento de la demanda eléctrica residencial anual, es decir para pronosticar valores que toma en determinados tiempos. En este proyecto de investigación solo se considera a nivel residencial, teniendo como en la necesidad la minimización de costos en el consumo de energía eléctrica para que sea representativo para el usuario, por el motivo que actualmente la lectura de contadores electromecánicos y electrónicos se realiza de forma manual y por lo general mensual, para determinar la facturación de energía. El desarrollo de la investigación se lleva a cabo a través de series de tiempo, donde se identifica que la demanda eléctrica no es estática, porque varía a través del tiempo por lo cual se eligió el método de pronóstico estático por series de tiempo, donde se determina la principales componente de una serie de tiempo. Para la modelación de la demanda residencial se requiere determinar una regresión lineal realizada con datos del consumo de energía eléctrica de datos históricos desde el 2013 facilitados por la Empresa Eléctrica Quito, para posteriormente realizar la predicción para el año 2016, programado y presentado en la herramienta informática Matlab, mostrando la predicción de la demanda individual y total. Para la optimización se desarrolla manualmente planteándose un límite referencial de consumo de forma heurística o por el método simplex para minimizar una función, para poder simular la reducción en los picos de la curva de respuesta de la demanda pronosticada

A framework for state-based time-series analysis and prediction

Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples