Towards a model of semi-supervised learning for the syntactic pattern recognition-based electrical load prediction system

Abstract

The paper is devoted to one of the key open problems of development of SPRELP system (the Syntactic Pattern Recognition-based Electrical Load Prediction System). The main module of SPRELP System is based on a GDPLL(k) grammar that is built according to the unsupervised learning paradigm. The GDPLL(k) grammar is generated by a grammatical inference algorithm. The algorithm doesn’t take into account an additional knowledge (the knowledge is partial and corresponds only to some examples) provided by a human expert. The accuracy of the forecast could be better if we took advantage of this knowledge. The problem of how to construct the model of a semi-supervised learning for SPRLP system that includes the additional expert knowledge is discussed in the paper. We also present several possible solutions.