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

The paper is devoted to one of the key open problems of development of SPRELP system (the Syntactic Pattern Recognitionbased 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.