Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling

Coccia, Gianluca; Mugnini, Alice; Polonara, Fabio; Arteconi, Alessia

doi:10.1016/j.energy.2021.119958

Cited by 36 publications

(3 citation statements)

References 40 publications

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“…Whereas many works in this field are based on classical strategies of predictive control with physical models, recent publications use machine-learning (ML)-based models for the optimization of these systems [24][25][26]. The usage of artificial neural networks (ANNs) with Long Short-Term Memory (LSTM) architectures [27] can be useful for the modeling and prediction of dynamical nonlinear systems [28,29].…”

Section: Introductionmentioning

confidence: 99%

Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast

Villegas Mier,

Dittmann,

Herzberg

et al. 2023

Energies

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Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.

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Section: Introductionmentioning

confidence: 99%

Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast

Villegas Mier,

Dittmann,

Herzberg

et al. 2023

Energies

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“…Even in other engineering fields, ML strategies are usually employed in this sense. Coccia et al (2021) [16] employed ANN models to simulate the cooling demand of a singlefamily house. Pervez et al ( 2021) proposes an ANN model to predict wind speed to be applied as a simulation model in a control scheme.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

et al. 2022

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A misusage of machine learning (ML) strategies is usually observed in the process systems engineering literature. This issue is even more evident when dynamic identification is performed. The root of this problem is the gradient explode and vanishing issue related to the recurrent neural networks training. However, after the advent of deep learning, these issues were mitigated. Furthermore, the problem of data structuration is often overlooked during the machine learning model identification in this field. In this scenario, this work proposes a guideline for identifying ML models for the different applications in process systems engineering, which are usually for simulation or prediction purposes. While using the proposed guideline, the work also identifies a virtual analyzer for a pressure swing adsorption unit. In these types of adsorption separations, it is usual that the measurement of the main properties is not done online. Therefore, the virtual analyzer is another contribution of this manuscript. The overall results demonstrate that even though the test provides good performance during the ML model identification, its quality might degenerate over the application domain if the model application is overlooked.

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“…[12][13][14] Subspace identification (SubID) models have also been used in the area of HVAC modelling and fault diagnosis. [15,16] In addition, machine learning techniques such as artificial neural networks (ANN) [17][18][19] and support vector machines (SVM) [20] have been utilized for modelling, control, and fault diagnosis of HVAC systems.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling HVAC systems

2022

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This paper addresses the problem of system identification for heating, ventilation, and air conditioning (HVAC) systems using a relatively small amount of data for the zone under consideration, by leveraging larger datasets for similar zones. To this end, a hybrid machine learning approach is developed where a pre‐trained recurrent neural network (RNN) model, trained on a large amount of data from a representative zone, is leveraged to build models for the other zones using a smaller amount of data. This is achieved by developing a hybrid model that integrates the pre‐trained RNN model with the models built using the subspace identification (SubID) technique to predict the residuals (differences between the real outputs and the predicted outputs from the pre‐trained RNN model) in the other zones. The effectiveness of the proposed hybrid approach is shown using real data collected from a multi‐zone fitness centre. The results demonstrate the superior performance of the hybrid approach over the cases where individual RNN and SubID models are directly developed using only the data from the zones in question.

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Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling

Cited by 36 publications

References 40 publications

Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast

Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast

Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling HVAC systems

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