Modelling of Demand Managing Based MIMO System for Price/Load Forecasting in Smart Grid
Since in smart grid the information flow has two ways between customers and manufacturers, the individual forecasting of electricity price and load won't be reasonable. Basically, short term forecasting is more difficult compare to medium and long term forecasting since the short term forecasting needs more accuracy in pick and valleys of a signal. Hereby, this dissertation addresses the multi-input multi-output system to forecast the electricity price and load in a simultaneous form for short term periods in a day and a week while consider their correlation. Therefore, a forecasting model is divided into three main parts, namely, preprocessing, learning engine and adjusted algorithm and each one of them carried out some new suggestions in order to improve forecasting accuracy. In first part consists of wavelet transform and mutual information to remove noisy term and select valuable input data, the second part used multi-input multi-output support vector machine to consider correlation between price and load signals and the last part employs an optimization algorithm (all parts will be with their derivation) in order to optimally set the learning engine control variables e.g. weight, Bayes, penalty factor and etc. As an earlier description, the principal aim of the smart grid is to improve the social benefits to the customers and the manufacturers so that the customer can change their usage profile that means they can shift their load peak on hour-to-hour in an economical way. This means a practical definition of demand-side management in a smart grid. Responsiveness of the market participants can improve the reliability of system operation as well as capital cost investments. As a result, in this dissertation the demand-side management is converted to an optimization problem in smart grid and its effect on load profile is evaluated. The proposed forecasting algorithms are evaluated on several real and well-known markets illustrating its high accuracy in a simultaneous forecast of electricity price and load. Moreover, the interactive effects of demand-side management programs on load factor (load curve) and electricity price signal are investigated by numerical indices. They are shown that the proposed multi input multi output forecasting algorithms have a good potential for simultaneous forecasting of electricity price and load in a smart grid compare to available methods. Moreover, demand-side management makes better load factor and forecasting accuracy in the lowest error value.
Keywords: Simultaneous forecast of electricity price and load, multi-input multi-output, demand-side management, wavelet transform, feature selection, optimization, smart grid.