Quality Versus Speed in Energy Demand Prediction Experience Report from an R&D project

Effective heat energy demand prediction is essential in combined heat power systems. The algorithms considered so far do not sufficiently take into account the computational costs and ease of implementation in industrial systems. However, computational cost is of key importance in edge and IoT systems, where prediction algorithms are constantly updated with new arriving data. In this paper, we propose two types of algorithms for heat demands prediction: (1) novel extensions to the algorithm originally proposed by E. Dotzauer and (2) based on a kind of autoregressive predictor. They were developed within an R&D project for a company operating a cogeneration system and for their real dataset. We evaluate the algorithms experimentally focusing on prediction quality and computational cost. The algorithms are compared against two state-of-the art artificial neural networks.

Automated summary

Effective heat energy demand prediction is crucial in combined heat power systems. Existing algorithms do not adequately consider computational costs and ease of implementation in industrial systems. This paper proposes two types of algorithms for heat demand prediction and evaluates them experimentally in terms of prediction quality and computational cost.