Forecasting Energy Consumption with the Data Reliability Estimatimation in the Management of Hybrid Energy System Using Fuzzy Decision Trees

One of the prospective directions to improving the quality of decision-making in the energy system with renewable energy sources (RES) is development and implementation of automation systems based on intelligent algorithms using historical data about energy consumption and power generation. Similar solutions in addition to reducing the load on the ecology, allow to obtain a significant economic effect due to optimum switching between energy sources, taking into account the current value of energy produced and the tariff plan. Decision trees (DT) have been recognized as interpretable, efficient, problem independent. and scalable architectures. In case of fuzzy representation, there is no procedure of automation tree building. In other words, existing approaches of building DT and fuzzy DT (FDT) cannot provide automatically generate fuzzy sets and fuzzy knowledge bases to build FDT. Paper presents: i) an optimal algorithm for switching among power sources based on the forecasted data and well known hybrid renewable energy systems (HRES) using genetic algorithm and multiagent technologies approach, ii) a new method of building fuzzy decision trees called decision trees based on fuzzy rules (DTFR). This method combines tree growing and pruning, to determine the structure of the FDT, to improve its generalization capabilities. This paper proposes a method (DTDR) considered as a variant of decision tree inductive using fuzzy set theory.