Abstract
The issue of very short-term forecasting is gaining more and more importance. It covers both the subject of power demand forecasting and forecasting of power generated in renewable energy sources. In particular, for the reason of necessity of ensuring reliable electricity supplies to consumers, it is very important in small energy micro-systems, which are commonly called microgrids. Statistical analysis of data for a sample big dynamics low voltage object will be presented in this paper. The object, in paper author’s opinion, belongs to a class of objects with difficulties in forecasting, in case of very short-term horizon. Moreover, forecasting methods, which can be applied to this type of forecasts, will be shortly characterized. Then results of sample very short-term ex post forecasts of power demand provided by several selected forecasting methods will be presented, as well as some qualitative analysis of obtained forecasts will be carried out. At the end of the paper observations and conclusions concerning analyzed subject, i.e. very short-term forecasting of power demand of big dynamics objects, will be presented.
Highlights
The issue of very short-term power demand forecasting is gaining more and more importance
Different kinds of neural networks were used in the papers for forecasting, such as Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Support Vector Regression (SVR) and Self-Organizing Map (SOM)
The issue of very short-term power demand forecasting of big dynamics object was the subject of this paper
Summary
The issue of very short-term power demand forecasting is gaining more and more importance. In the publications being analyzed, the objects of big dynamics in the power demand level are very rarely the subject of very short-term forecasting activities. This is the clue and the key aspect of this paper. In the further part of the paper the results of sample very short-term forecasting processes for the presented component of microgrid, obtained with the use of the following methods (models): naive model, weighted moving average models, auto-regression models, multiple linear regression models, MLP type neural networks and Radial Basis Function type neural networks will be given. It is a unidirectional neural network consisting of input layer, hidden (radial) layer and output layer [9]. The parameters (weights) of individual models were selected using optimization methods (BFGS algorithm for MLP networks, RBFT algorithm for RBF networks and Newton's algorithm for other models for which the values of parameters need to be chosen)
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