Publications
9 results found
Yu D, Liu T, Wang K, et al., 2024, Transformer based day-ahead cooling load forecasting of hub airport air-conditioning systems with thermal energy storage, Energy and Buildings, Vol: 308, ISSN: 0378-7788
The air conditioning system constitutes more than half of the total energy demand in hub airport buildings. To enhance the energy efficiency and to enable intelligent energy management, it is vital to build an accurate cooling load prediction model. However, the current models face challenges in dealing with dispersed load patterns and lack interpretability when black box approaches are adopted. To tackle these challenges, we propose a novel k-means-Temporal Fusion Transformer (TFT) based hybrid load prediction model. Specifically, the daily load patterns are grouped using an improved k-means clustering method that considers both input feature weights and dynamic time warping (DTW) distances. Additionally, the statistical features of the clustering output are inputted into the TFT. By further incorporating context information, the integration of data between different schema categories is achieved, thus reducing errors that may occur during the transition process. As a result, the prediction performance and interpretability are significantly improved. The Chongqing Jiangbei Airport T3A terminal is used as a case study, and experiments are conducted using cooling data from the No.1 energy station, as well as the airport traffic data and the meteorological station data. Results are compared with other mainstream models, confirming that the proposed day-ahead load forecasting model achieves improvements in several performance indicators, including MAE, MAPE, CV-RMSE, and R2, which are 384 kW, 3%, 5%, and 0.058 respectively.
Ahmed A, Rio-Chanona EAD, Mercangoez M, 2023, Linearizing nonlinear dynamics using deep learning, COMPUTERS & CHEMICAL ENGINEERING, Vol: 170, ISSN: 0098-1354
Zagorowska M, Degner M, Ortmann L, et al., 2023, Online Feedback Optimization of Compressor Stations with Model Adaptation using Gaussian Process Regression, JOURNAL OF PROCESS CONTROL, Vol: 121, Pages: 119-133, ISSN: 0959-1524
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- Citations: 2
Liu T, Chen S, Yang P, et al., 2023, Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement, IEEE Transactions on Control Systems Technology, Vol: 32, Pages: 384-398, ISSN: 1063-6536
As an emerging machine learning technique, lifelong learning is capable of solving multiple consecutive tasks based on previously accumulated knowledge. Although this is highly desired for streaming process prediction in industry, lifelong learning methods have so far failed to gain applications to mainstream adaptive predictive modeling of time-varying industrial processes. This is because when faced with a new data batch, existing lifelong learning approaches need both input and output data to construct local predictors before knowledge transfer can succeed. But in many process industries, the process output data are hard to measure online and it often takes time to acquire them from off-site laboratory analysis. This delayed acquisition of target output data makes it challenging to apply lifelong learning and other existing adaptive mechanisms to dynamic industrial processes with delayed process output measurement. To overcome this difficulty, this article proposes a novel lifelong learning framework that can rapidly predict new data batches with input data only before the arrival of the process output measurement. Specifically, we propose to incorporate process input information into lifelong learning via coupled dictionary learning, to enable the prediction of new batches without target output data. The input feature is linked with a local predictor through two dictionaries that are coupled by a joint sparse representation. Because of the learned coupling between the two spaces, the local predictor for the new batch can be reconstructed by knowledge transfer given only process inputs. Two industrial case studies are used to evaluate the effectiveness of our proposed framework and reveal the intrinsic learning mechanism of our lifelong process modeling to perform knowledge base (KB) adaptation.
Zhuang Y, Liu Y, Ahmed A, et al., 2022, A hybrid data-driven and mechanistic model soft sensor for estimating CO<sub>2</sub> concentrations for a carbon capture pilot plant, COMPUTERS IN INDUSTRY, Vol: 143, ISSN: 0166-3615
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- Citations: 5
Ahmed A, Zagorowska M, Del Rio-Chanona EA, et al., 2022, Application of gaussian processes to online approximation of compressor maps for load-sharing in a compressor station, 2022 European Control Conference (ECC), Publisher: IEEE
Devising optimal operating strategies for a compressor station relies on the knowledge of compressor characteristics. As the compressor characteristics change with time and use, it is necessary to provide accurate models of the characteristics that can be used in optimization of the operating strategy. This paper proposes a new algorithm for online learning of the characteristics of the compressors using Gaussian Processes. The performance of the new approximation is shown in a case study with three compressors. The case study shows that Gaussian Processes accurately capture the characteristics of compressors even if no knowledge about the characteristics is initially available. The results show that the flexible nature of Gaussian Processes allows them to adapt to the data online making them amenable for use in real-time optimization problems.
Korkmaz BS, Mercangoz M, 2022, Data Driven Modelling of Centrifugal Compressor Maps for Control and Optimization Applications, European Control Conference (ECC), Publisher: IEEE, Pages: 2260-2265
Ahmed A, Del Rio-Chanona EA, Mercangoz M, 2022, Learning Linear Representations of Nonlinear Dynamics Using Deep Learning, 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS), Publisher: ELSEVIER, Pages: 162-169, ISSN: 2405-8963
Borghesan F, Zagorowska M, Mercangoz M, 2022, Unmanned and Autonomous Systems: Future of Automation in Process and Energy Industries, 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS), Publisher: ELSEVIER, Pages: 875-882, ISSN: 2405-8963
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- Citations: 1
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