Improved day ahead heating demand forecasting by online correction methods

Abstract

To reduce the heating and cooling energy demand of buildings and districts novel control strategies are constantly being developed that require information on the future demand of the controlled entity. Demand forecasting is commonly done with deterministic white box models or fitted grey-box models, however, recently more and more data and machine learning based approaches are being developed. All approaches have weaknesses: white-box models require major modelling effort, grey-box approaches are limited by their model or parameter complexity and machine learning is dependent on hyperparameters, some of which are randomly chosen, and therefore considered unreliable. Here we develop a forecasting approach based on Artificial Neural Networks (ANN) and introduce error correction methods based on online learning and the learned autocorrelation of the forecasting error. We compare the approach to other regression based and grey-box methods in a real case study of a small-scale district energy system with mixed use and unknown lower-level control. We show that the proposed method outperforms the other forecasting methods in terms of average error and coefficient of determination. We further demonstrate that in our case study the error correction methods significantly reduce variance in ANN performance created by randomly initialized parameters in the networks.

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

“…ML uses several forecasting methods such as Neural Network, Fuzzy Neural Network, Artificial Neural Network, Recurrent Neural Networks, Genetic Algorithm, Support Vector Regression, Random Forest Regressor, Decision Tree Regressor etc. to tackle demand forecasting problems (Abbasimehr et al, 2020;Bünning et al, 2020;Carbonneau et al, 2008;Chang et al, 2009;Chen & Lu, 2017;Claveria et al, 2016;Feng & Wang, 2017;Qiu et al, 2017). Solving demand forecasting problems is not easy because there are many factors that affect demand; one of them is advertisements (Ali et al, 2009;Okrent & MacEwan, 2014;Zheng & Henneberry, 2010).…”

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

“…Commercial case studies were found applied to offices [72][73][74][75][76][77][78], retail [35], hotels [79], and non-specified (show in references [38,43,45,80]). Focusing on office buildings, it was observed that thermal loads accounted for approximately half of the case studies applied with a predominance of cooling load applications.…”

“…DFFNNs have shown promising performance results for forecasting building thermal energy loads. For instance, Bünning et al applied a DFFNN in order to forecast the heating demand of an office building [75]. The model was compared with other prominent black-box and grey-box based models for a forecast horizon of a day-ahead.…”

Robust MPC with data-driven demand forecasting for frequency regulation with heat pumps

“…To mitigate the problem of high prediction variance between individual networks [37], [38] and [39] propose ensemble methods in the context of building demand prediction. As ensemble methods have the disadvantage of being computationally expensive, we have developed and validated correction methods based on online learning and error autocorrelation correction methods, which both decrease variance and increase accuracy, while avoiding the disadvantages of ensemble methods [40].…”

“…In this work, we combine the robust MPC for frequency regulation approach presented in [41] with the forecasting methods presented in [40] to offer frequency regulation reserves with a system comprising a ground-source heat pump and water buffer storage that meet the heating demand of a mixed-use building. The robust MPC approach is a further development of [15], which was adapted to this heating system.…”