Neural Network Based Central Heating System Load Prediction and Constrained Control

Wang, Hongwei; Tu, Fangwen; Tu, Baofeng; Guo, Feng; Yuan, Guangming; Ren, Hao; Dong, Jiarong

doi:10.1155/2018/2908608

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Figure 4 provides more details on the ML techniques that are used in the articles. ANNs [87,88], by far, show the highest number of appearances, followed by instance-based algorithms such as SVMs [89,90]. Clustering algorithms such as the k-means algorithm are used frequently to split datasets into groups of maximum similarity.…”

Section: Techniques and Input Datamentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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Section: Techniques and Input Datamentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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“…In [5], a regional automatic control strategy is proposed to designate temperature monitor and valve on the user end for the heating quality enhancement. A load prediction based multiple constrained control is put forward in [6] to handle the accuracy of water temperature supply and input water volume constraints. However, none of the above mentioned research considers the input delay due to the signal transmission inside the system and the valve operating time.…”

Section: Introductionmentioning

confidence: 99%

Central Heating System Constrained Control with Input Delay Based on Neural Networks

Wang

Guo

et al. 2018

Mathematical Problems in Engineering

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An output constrained control with input delay is proposed for a central heating system. Due to the delay of signal transmission and valves opening time, an input delay is considered into the system and an auxiliary system is employed to handle this issue by converting the delayed input into a delay-free one. Moreover, to ensure the output supply water temperature within a limited range, Barrier Lyapunov algorithm is involved to achieve desired control accuracy. Finally, external disturbance and model uncertainty are incorporated into the dynamic system and neural networks (NN) are trained in an online fashion for the compensation. The stability of the control system is guaranteed through rigorous Lyapunov analysis and the excellent control performance over traditional PID control is demonstrated via numerical simulation study.

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A Modified MPC-Based Optimal Strategy of Power Management for Fuel Cell Hybrid Vehicles

Chen

et al. 2020

IEEE/ASME Trans. Mechatron.

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Neural Network Based Central Heating System Load Prediction and Constrained Control

Cited by 3 publications

References 33 publications

Modeling Energy Demand—A Systematic Literature Review

Modeling Energy Demand—A Systematic Literature Review

Central Heating System Constrained Control with Input Delay Based on Neural Networks

A Modified MPC-Based Optimal Strategy of Power Management for Fuel Cell Hybrid Vehicles

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