Enhancing Heat Production by AI-Driven Performance Analysis, Impact of Electricity Price Variations in Day-Ahead Market

In this paper, the impact of electricity price variation on operational expenses is studied for the heat production unit, for which an AI-based algorithm is used to calculate the unit's performance. The unit is connected to the storage tank to assist the production unit in overproducing heat when the electricity price is high. In this study, an ANN-based method is intended to adopt a heat production unit to model the unit's performance efficiently. Given this situation, this method is well-suited for modeling tasks and does not need system-specific knowledge. A value of 0.066 for RMS and 0.999 for R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value are achieved with the suggested adjustment of determining parameters. The improvement performance mechanism could improve the system's performance by reducing the system's operational cost by up to 37% when fluctuation occurs at low energy price conditions.