Using an artificial neural network to improve the accuracy of IHP controller settings using the example of higher education buildings

Abstract

Objective. The objective of the study is to create a decision support system (DSS) that automatically analyzes sensor data, processes statistical data, and alerts the operator to the need for PLC programming, providing calculation results on the predicted technical and economic effect using an artificial neural network.

Method. The study is based on methods for constructing intelligent control systems for technological processes and production.

Result. The study examined the possibilities of increasing the efficiency of the automated process control system by improving the accuracy of the IHP controller settings. A method for improving the accuracy of the automated process control system by using an artificial neural network is considered. It is proposed to perform forecast calculations based on hydrometeorological center forecasts with a forecast period of 4-6 hours ahead. A set of additional sensors necessary for improving the forecast accuracy is proposed. The additional capital expenditures will pay for themselves within 2 heating periods.

Conclusion. Existing approaches to the digitalization of heat power equipment control methods are considered. The use of an artificial neural network allows for increasing the accuracy of the IHP controller settings.