Staff profile

Peter Matthews is an Associate Professor in Design Informatics at the Department of Engineering. His research in Design Informatics utilises data mining and machine learning tools to critically appraise technical data sets, such as operational sensor data from wind turbines (typically from SCADA systems). Gaining insights to the underlying processes governing these systems gives us a deeper understanding, which ultimately leads to improved design for the next generation of system.

The primary goal for machine learning with technical data is to be able to predict with sufficient warning when a machine is likely to fail. This prognostic ability has the potential of providing significant cost savings to industry: maintenance can be performed at the optimal time, allowing better planning, and the risk of incurring secondary damage is mitigated. Dr Matthews has led several successful projects which have accurately predicted failures for specific sub-systems (see results with Chen (2015), Godwin (2013), and Smith (2015)).

Another important component of Dr Matthews’ research is the production of tractable and humanly-understandable rules. Tractable rules require a much simpler validation process and are therefore more useable by system operators and designers when seeking to improve a system’s performance.

Wind Energy

Dr Matthews' wind energy research is primarily in data mining SCADA and other wind turbine operational data. This research is primarily aimed at developing diagnostic and prognostic measures for individual wind turbine health. The approach taken is based around statistical modelling of healthy wind turbines, and then comparing live wind turbines against this healthy model. Other methods (eg physics based) are under development as well, again using ‘big data’ approaches to validate.

In addition to SCADA analysis, Dr Matthews has directed research in wake optimisation and maintenance strategy simulation. The wake optimisation research has delivered a workable dynamic wind farm controller that can minimise the effect of in-farm wakes on total production. The maintenance strategy simulation provided a Monte Carlo based approach for developing and testing alternative off-shore wind farm maintenance strategies.

Much of the Wind Energy research is undertaken with industrial partners Ørsted Energy and Maia Eolis (now Engie Green).

Energy Distribution

The energy distribution sector has a broad range of customers, from domestic through to large industrial customers. All these customers use electricity in different ways, and their consumption is recorded using SCADA systems. Dr Matthews’ research in the Energy Distribution sector centres around data mining these SCADA databases of thousands of customers, as well as hundreds of electrical substations, to gain better understanding of the overall picture of electricity use. Dr Matthews has also directed research to forecast the demand increases at substation level using substation demographic customer profiles.

Much of this research is undertaken with Northern Powergrid.

Design Analysis

The Design Analysis research is based on data mining, but with considerably smaller datasets. Here, the aim is to extract the tacit rules the human designers have applied, and gain better understanding of the design domain through making these rules explicit. Design data often contains greater textual information, and so text mining approaches have also been applied with interesting results. Other techniques that have been used include Bayesian Belief Network and p-boxes. These techniques have been used to mitigate against the greater uncertainty levels that can be associated with early designs.

This research has been undertaken with Rolls-Royce (Aerospace) and BAE Systems.

• Monte Carlo methods
• Engineering Uncertainty modelling and management
• Knowledge Management
• Engineering Design
• Artificial Intelligence and Machine Learning
• Design process
• Game theory
• Data mining
• Wind Energy

Authored book

• Design of Sustainable Product Lifecycles
  
  Aldinger, L., Alzaga, A., Baguley, P., Bittner, T., Boër, C., Donna, B., Bramley, A., Brissaud, D., Bünting, F., Bufardi, A., Chryssolouris, G., Colledani, M., Dinkelmann, M., Dori, D., Draghici, G., Draghici, A., Du Preez, N. D., Enparantza, R., Fischer, A., … Xirouchakis, P. (2009). Design of Sustainable Product Lifecycles. Springer Verlag. https://doi.org/10.1007/978-3-540-79083-9

Chapter in book

• Robust Statistical Methods for Rapid Data Labelling
  
  Godwin, J., & Matthews, P. (2014). Robust Statistical Methods for Rapid Data Labelling. In V. Bhatnagar (Ed.), Data mining and analysis in the engineering field. (pp. 107-141). IGI Global. https://doi.org/10.4018/978-1-4666-6086-1.ch007
• Implementing Digital Enterprise Technologies for Agile Design in the Virtual Enterprise
  
  Lomas, C., Maropoulos, P., & Matthews, P. (2007). Implementing Digital Enterprise Technologies for Agile Design in the Virtual Enterprise. In P. Cunha & P. Maropoulos (Eds.), Digital enterprise technology : perspectives and future challenges. (pp. 177-184). Springer Verlag. https://doi.org/10.1007/978-0-387-49864-5_20

Conference Paper

• An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning
  
  Liu, J., Kazemtabrizi, B., Du, H., Matthews, P., & Sun, H. (2025). An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning. In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE. https://doi.org/10.1109/IECON55916.2024.10905784
• Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems
  
  Correa-Delval, M., Sun, H., Matthews, P. C., & Chiu, W.-Y. (2022). Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems. Presented at 2022 5th International Conference on Renewable Energy and Power Engineering (REPE 2021), Beijing, China. https://doi.org/10.1109/repe55559.2022.9949497
• Appliance Classification using BiLSTM Neural Networks and Feature Extraction
  
  Correa-Delval, M., Sun, H., Matthews, P., & Jiang, J. (2021). Appliance Classification using BiLSTM Neural Networks and Feature Extraction. Presented at IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Espoo, Finland. https://doi.org/10.1109/isgteurope52324.2021.9640061
• Stochastic environmental and economic dispatch of power systems with virtual power plant in energy and reserve markets
  
  Hua, W., Li, D., Sun, H., Matthews, P., & Meng, F. (2018). Stochastic environmental and economic dispatch of power systems with virtual power plant in energy and reserve markets. In 2nd International Conference on Power and Renewable Energy (ICPRE 2017) : Chengdu, China, September 20-23, 2017 ; proceedings. (pp. 654-658).
• SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder
  
  Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder. In A. E. P. Villa, P. Masulli, & A. J. Pons Rivero (Eds.), Artificial neural networks and machine learning – ICANN 2016 : 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016 ; proceedings. Part II. (pp. 423-430). Springer Verlag. https://doi.org/10.1007/978-3-319-44781-0_50
• Analysis of Two Onshore Wind Farms with a Dynamic Farm Controller
  
  Ahmad, T., Girard, N., Kazemtabrizi, B., & Matthews, P. (2015, November 20). Analysis of Two Onshore Wind Farms with a Dynamic Farm Controller. Presented at EWEA 2015, Paris, France.
• Onshore Wind Farm Fast Wake Estimation Method: Critical Analysis of the Jensen Model
  
  Sidwell, N., Ahmad, T., & Matthews, P. (2015, November 19). Onshore Wind Farm Fast Wake Estimation Method: Critical Analysis of the Jensen Model. Presented at EWEA 2015, Paris, France.
• Characterisation of Electrical Loading Experienced by a Nacelle Power Converter
  
  Smith, C., Wadge, G., Crabtree, C., & Matthews, P. (2015, November 19). Characterisation of Electrical Loading Experienced by a Nacelle Power Converter. Presented at EWEA 2015, Paris, France.
• Evaluation of Synthetic Wind Speed Time Series for Reliability Analysis of Offshore Wind Farms
  
  Smith, C., Crabtree, C., & Matthews, P. (2015, November 19). Evaluation of Synthetic Wind Speed Time Series for Reliability Analysis of Offshore Wind Farms. Presented at EWEA 2015, Paris, France.
• Dynamic Wind Farm Controller
  
  Ahmad, T., Matthews, P., Kazemtabrizi, B., & Smith, C. (2015, September 30). Dynamic Wind Farm Controller. Presented at 11th EAWE PhD Seminar on Wind Energy in Europe., Stuttgart, Germany.
• Experimental Set-up for Applying Wind Turbine Operating Profiles to the Nacelle Power Converter
  
  Smith, C., Crabtree, C., & Matthews, P. (2015, September 25). Experimental Set-up for Applying Wind Turbine Operating Profiles to the Nacelle Power Converter. Presented at 11th EAWE PhD Seminar, Stuttgart, Germany.
• Analysis of clustering techniques on load profiles for electrical distribution
  
  Akperi, B., & Matthews, P. (2014). Analysis of clustering techniques on load profiles for electrical distribution. In POWERCON 2014 Chengdu : 2014 International Conference on Power System Technology : Towards green, efficient and smart power system. Proceedings of a meeting held 20-22 October 2014, Chengdu, China. (pp. 1142-1149). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/powercon.2014.6993986
• Determining the Wind Speed Distribution within a Wind Farm considering Site Wind Characteristics and Wake Effects
  
  Ahmad, T., Smith, C., Matthews, P., Crabtree, C., & Kazemtabrizi, B. (2014, October 1). Determining the Wind Speed Distribution within a Wind Farm considering Site Wind Characteristics and Wake Effects. Presented at 10th PhD Seminar on Wind Energy in Europe, EAWE., Orléans, France.
• Modelling and Evaluation of Wind Speed Time Series for Reliability Analysis of Offshore Wind Farms
  
  Smith, C., Crabtree, C., Matthews, P., & Kazemtabrizi, B. (2014, October 1). Modelling and Evaluation of Wind Speed Time Series for Reliability Analysis of Offshore Wind Farms. Presented at 10th PhD Seminar on Wind Energy in Europe, EAWE., Orléans, France.
• Analysis of customer profiles on an electrical distribution network
  
  Akperi, B., & Matthews, P. (2014). Analysis of customer profiles on an electrical distribution network. In M. Conlon, D. D. Micu, M. Al-Tai, & C. Ferreira (Eds.), Proceedings of 2014 49th International Universities Power Engineering Conference (UPEC) : 2-5 September 2014, Cluj-Napoca, Romania. (pp. 1-6). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/upec.2014.6934624
• Automated Wind Turbine Pitch Fault Prognosis using ANFIS
  
  Chen, B., Matthews, P., & Tavner, P. (2013, February 5). Automated Wind Turbine Pitch Fault Prognosis using ANFIS. Presented at EWEA 2013, Vienna, Austria.
• Structural optimisation using boundary element based level set method
  
  Ullah, B., Trevelyan, J., & Matthews, P. (2012). Structural optimisation using boundary element based level set method. Presented at Proceedings of the 20th UK conference of the Association for Computational Mechanics in Engineering (ACME), University of Manchester, Manchester.
• Bayesian Project Monitoring
  
  Matthews, P., & Philip, A. (2011). Bayesian Project Monitoring (S. Culley, B. Hicks, T. McAloone, T. Howard, & P. Clarkson, Eds.). Design Society.
• Comparing Stochastic Design Decision Belief Models: Pointwise versus Interval Probabilities
  
  Matthews, P. (2010). Comparing Stochastic Design Decision Belief Models: Pointwise versus Interval Probabilities. In J. Gero (Ed.), Design Computing and Cognition (pp. 327-345). Springer Verlag.
• Pre-emptive Concurrent Design Planning and Scheduling
  
  Matthews, P., & Coates, G. (2007, August). Pre-emptive Concurrent Design Planning and Scheduling. Presented at 16th International Conference on Engineering Design, Paris, France.
• Meta-Design for Agile Concurrent Product Design in the Virtual Enterprise
  
  Lomas, C. D. W., & Matthews, P. C. (2007). Meta-Design for Agile Concurrent Product Design in the Virtual Enterprise (P. C. Matthews, Ed.). Institution of Engineering and Technology (IET).
• Stochastic based Pre-emptive Planning and Scheduling
  
  Matthews, P., & Coates, G. (2007, July). Stochastic based Pre-emptive Planning and Scheduling. Presented at 10th International Conference on Agile Manufacturing, Durham, UK.
• Bayesian Networks for Engineering Design Decision Support
  
  Matthews, P. (2007). Bayesian Networks for Engineering Design Decision Support. In S. I. Ao, L. Gelman, D. W. L. Hukins, A. Hunter, & A. M. Korsunsky (Eds.), 2007 International Conference of Data Mining and Knowledge Engineering, ICDMKE07, 2-4 July 2007, London, UK ; proceedings. (pp. 284-289). International Association of Engineers.
• Implementing Digital Enterprise Technologies for Agile Design in the virtual enterprise
  
  Lomas, C., Wilkinson, J., Matthews, P., & Maropoulos, P. (2006). Implementing Digital Enterprise Technologies for Agile Design in the virtual enterprise (P. Cunha & P. Maropoulos, Eds.). CIRP.
• Measuring design process agility for the single company product development process
  
  Lomas, C., Wilkinson, J., Maropoulos, P., & Matthews, P. (2006). Measuring design process agility for the single company product developmentprocess (H. Andersin & A. Verma, Eds.).
• A Methodology for Negotiating Change Propagation in Agile Design.
  
  Matthews, P., Lomas, C., & Maropoulos, P. (2006). A Methodology for Negotiating Change Propagation in Agile Design (H. Andersin & A. Verma, Eds.). International Society for Agile Manufacturing.
• Bayesian Networks for Design
  
  Matthews, P. (2006). Bayesian Networks for Design (J. Gero, Ed.).
• Agile resource allocation through pre-emptive planning .
  
  Matthews, P., Coates, G., & Lomas, C. (2006). Agile resource allocation through pre-emptive planning (H. Andersin & A. Verma, Eds.).
• Machine learning stochastic design models
  
  Matthews, P. (2005). Machine learning stochastic design models. In A. Samuel & W. Lewis (Eds.), 15th International Conference on Engineering Design, ICED05, 15-18 August 2005, Melbourne, Australia ; proceedings.. Design Society.
• Development of a simple information pump
  
  Matthews, P., Keegan, J., & Robson, J. (2005). Development of a simple information pump. In A. Samuel & W. Lewis (Eds.), Proceedings of the International Conference on Engineering Design.. Design Society.
• Partner Profiling to Support Agile Design
  
  Armoutis, N., Matthews, P., Lomas, C., & Maropoulos, P. (2005). Partner Profiling to Support Agile Design. In M. Zäh (Ed.), First International Conference on Changeable, Agile, Reconfigurable and Virtual Production (pp. 267-272).
• An Agile Digital Enterprise Technology Cost Engineering Tool
  
  Baguley, P., Qaqish, T., Matthews, P., & Maropoulos, P. (2005). An Agile Digital Enterprise Technology Cost Engineering Tool. In H. Andersin (Ed.), International Conference on Agile Manufacturing 2005. International Society of Agile Manufacturing.
• Foundations of an Agile Design Methodology
  
  Matthews, P., Lomas, C., Armoutis, N., & Maropoulos, P. (2005). Foundations of an Agile Design Methodology. In H. Andersin (Ed.), International Conference on Agile Manufacturing 2005. International Society of Agile Manufacturing.
• Verification of Event Impact Levels for an Agile Design Framework
  
  Lomas, C., Matthews, P., Armoutis, N., & Maropoulos, P. (2005). Verification of Event Impact Levels for an Agile Design Framework. Presented at Proceedings of the 2nd International Conference on Manufacture Engineering, Kalithea.
• Using Self Organizing Maps as a Design Exploration Tool
  
  Matthews, P., & Wallace, K. (2003). Using Self Organizing Maps as a Design Exploration Tool. In A. Folkeson, K. Gralen, M. Norell, & U. Sellgren (Eds.), Proceedings of the International Conference on Engineering Design (pp. 597-598). Design Society.
• Inducing Change Propagation Models using Previous Designs
  
  Matthews, P., & Lowe, D. (2003). Inducing Change Propagation Models using Previous Designs. In A. Folkeson, K. Gralén, M. Norell, & U. Sellgren (Eds.), Proceedings of the International Conference on Engineering Design (pp. 443-444). Design Society.
• New techniques for design knowledge exploration: A comparison of three data grouping approaches
  
  Matthews, P., Langdon, P., & Wallace, K. (2001). New techniques for design knowledge exploration: A comparison of three data grouping approaches. In S. Culley, A. Duffy, C. McMahon, & K. Wallace (Eds.), Proceedings of the International Conference on Engineering Design (pp. 107-114). Professional Engineering Publishing (Institution of Mechanical Engineers).
• Extracting Experience through Protocol Analysis
  
  Matthews, P., Ahmed, S., & Aurisicchio, M. (2001). Extracting Experience through Protocol Analysis. In F. Kurfess & M. Hilario (Eds.), Integrating Data Mining and Knowledge Management. Technical Report CPSLO-CSC-01-03, Department of Computer Science, California Polytechnic State University.
• Design Heuristics Extraction: Acquiring engineering knowledge from previous designs
  
  Matthews, P., Wallace, K., & Blessing, L. (2000). Design Heuristics Extraction: Acquiring engineering knowledge from previous designs. In J. Gero (Ed.), Artificial Intelligence in Design 2000 (pp. 435-453). Kluwer, Dordrecht.
• Conceptual Evaluation using Neural Networks
  
  Matthews, P., Blessing, L., & Wallace, K. (1999). Conceptual Evaluation using Neural Networks. In U. Lindemann, H. Birkhofer, H. Meerkamm, & S. Vanja (Eds.), Proceedings of the 12th International Conference on Engineering Design (pp. 1777-1780).
• Active Design Support with a Hierarchical Blackboard Structure
  
  Ball, N., & Matthews, P. (1998). Active Design Support with a Hierarchical Blackboard Structure. In Poster Proceedings of the Third International Conference on Adaptive Computing in Design and Manufacture (pp. 58-61). University of Plymouth.
• Using a Guideline Database to Support Design Emergence: A Proposed System based on a Designer's Workbench
  
  Matthews, P. (1998). Using a Guideline Database to Support Design Emergence: A Proposed System based on a Designer’s Workbench. In S. Chase & L. Schmidt (Eds.), Workshop Proceedings of the AID’98: Emergence in Design (pp. 13-18).
• Managing Conceptual Design Objects: An Alternative to Geometry
  
  Ball, N., Matthews, P., & Wallace, K. (1998). Managing Conceptual Design Objects: An Alternative to Geometry. In J. Gero & F. Sudweeks (Eds.), Artificial Intelligence in Design ’98 (pp. 67-86). Kluwer, Dordrecht.
• Towards Mechanical Design Object Reuse: The Description, Retrieval and Classification of Cases
  
  Charlton, C., Ball, N., & Matthews, P. (1998). Towards Mechanical Design Object Reuse: The Description, Retrieval and Classification of Cases. In J. Gero & F. Sudweeks (Eds.), Artificial Intelligence in Design ’98 (pp. 311-325). Kluwer, Dordrecht.
• Constraint Based Templates for Design Re-use
  
  Murdoch, T., Ball, N., & Matthews, P. (1997). Constraint Based Templates for Design Re-use. In A. Riitahuhta (Ed.), Proceedings of the 11th International Conference on Engineering Design (pp. 267-270). Tampere University of Technology.

Doctoral Thesis

• The Application of Self Organizing Maps in Conceptual Design
  
  Matthews, P. (2002). The Application of Self Organizing Maps in Conceptual Design [Thesis]. Department of Engineering, University of Cambridge.

Journal Article

• Transactive Energy and Flexibility Provision in Multi-microgrids using Stackelberg Game
  
  Hua, W., Xiao, H., Pei, W., Chiu, W.-Y., Jiang, J., Sun, H., & Matthews, P. (2023). Transactive Energy and Flexibility Provision in Multi-microgrids using Stackelberg Game. CSEE Journal of Power and Energy Systems, 9(2), 505-515. https://doi.org/10.17775/cseejpes.2021.04370
• Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms
  
  Ahmad, T., Basit, A., Ahsan, M., Coupiac, O., Girard, N., Kazemtabrizi, B., & Matthews, P. (2019). Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms. Energies, 12(7), Article 1266. https://doi.org/10.3390/en12071266
• Fast Processing Intelligent Wind Farm Controller for Production Maximisation
  
  Ahmad, T., Basit, A., Anwar, J., Coupiac, O., Kazemtabrizi, B., & Matthews, P. (2019). Fast Processing Intelligent Wind Farm Controller for Production Maximisation. Energies, 12(3), Article 544. https://doi.org/10.3390/en12030544
• Stochastic environmental and economic dispatch of power systems with virtual power plant in energy and reserve markets
  
  Hua, W., Li, D., Sun, H., Matthews, P., & Meng, F. (2018). Stochastic environmental and economic dispatch of power systems with virtual power plant in energy and reserve markets. International Journal of Smart Grid and Clean Energy, 7(4), 231-239. https://doi.org/10.12720/sgce.7.4.231-239
• Field Implementation and Trial of Coordinated Control of Wind Farms
  
  Ahmad, T., Coupliac, O., Petit, A., Guignard, S., Girard, N., Kazemtabrizi, B., & Matthews, P. (2018). Field Implementation and Trial of Coordinated Control of Wind Farms. IEEE Transactions on Sustainable Energy, 9(3), 1169-1176. https://doi.org/10.1109/tste.2017.2774508
• Method for Designing a High Capacity Factor Wide Area Virtual Wind Farm
  
  Trenkel-Lopez, M., & Matthews, P. (2018). Method for Designing a High Capacity Factor Wide Area Virtual Wind Farm. IET Renewable Power Generation, 12(3), 351-358. https://doi.org/10.1049/iet-rpg.2017.0396
• Impact of wind conditions on thermal loading of PMSG wind turbine power converters
  
  Smith, C., Crabtree, C., & Matthews, P. (2017). Impact of wind conditions on thermal loading of PMSG wind turbine power converters. IET Power Electronics, 10(11), 1268-1278. https://doi.org/10.1049/iet-pel.2016.0802
• Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition
  
  Chen, B., Matthews, P., & Tavner, P. (2015). Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition. IET Renewable Power Generation, 9(5), 503-513. https://doi.org/10.1049/iet-rpg.2014.0181
• Structural optimisation based on the boundary element and level set methods
  
  Ullah, B., Trevelyan, J., & Matthews, P. (2014). Structural optimisation based on the boundary element and level set methods. Computers and Structures, 137, 14-30. https://doi.org/10.1016/j.compstruc.2014.01.004
• Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS
  
  Chen, B., Matthews, P., & Tavner, P. (2013). Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS. Expert Systems With Applications, 40(17), 6863-6876. https://doi.org/10.1016/j.eswa.2013.06.018
• Classification and Detection of Electrical Control System Faults Through SCADA Data Analysis
  
  Godwin, J., Matthews, P., & Watson, C. (2013). Classification and Detection of Electrical Control System Faults Through SCADA Data Analysis. Chemical Engineering Transactions., 1, 985-990. https://doi.org/10.3303/cet1333165
• Through-Life Systems Engineering Design & Support with SysML
  
  Chandler, S., & Matthews, P. (2013). Through-Life Systems Engineering Design & Support with SysML. Procedia CIRP, 11, 425-430. https://doi.org/10.1016/j.procir.2013.07.002
• Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis
  
  Godwin, J., & Matthews, P. (2013). Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis. International Journal of Prognostics and Health Management., 4, Article 016.
• Bayesian project diagnosis for the construction design process
  
  Matthews, P., & Philip, A. (2012). Bayesian project diagnosis for the construction design process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 26(4), 375-391. https://doi.org/10.1017/s089006041200025x
• Challenges to Bayesian decision support using morphological matrices for design: empirical evidence
  
  Matthews, P. (2011). Challenges to Bayesian decision support using morphological matrices for design: empirical evidence. Research in Engineering Design, 22(1), 29-42. https://doi.org/10.1007/s00163-010-0094-1
• A methodology for quantitative estimates for the work and disturbance transformation matrices
  
  Matthews, P. C., & Lomas, C. D. (2010). A methodology for quantitative estimates for the work and disturbance transformation matrices. Journal of Engineering Design, 21(4), 413-425. https://doi.org/10.1080/09544820802310909
• Linking design and manufacturing domains via web-based and enterprise integration technologies
  
  Cheung, W., Maropoulos, P., & Matthews, P. (2010). Linking design and manufacturing domains via web-based and enterprise integration technologies. International Journal of Computer Applications in Technology, 37(3/4), 182-197. https://doi.org/10.1504/ijcat.2010.031934
• Advanced product development integration architecture: An out-of-box solution to support distributed production networks
  
  Cheung, W., Matthews, P., Gao, J., & Maropoulos, P. (2008). Advanced product development integration architecture: An out-of-box solution to support distributed production networks. International Journal of Production Research, 46(12), 3185-3206. https://doi.org/10.1080/00207540601039767
• A Bayesian support tool for morphological design
  
  Matthews, P. (2008). A Bayesian support tool for morphological design. Advanced Engineering Informatics, 22(2), 236-253. https://doi.org/10.1016/j.aei.2007.05.001
• Establishing agile supply networks through competence profiling
  
  Armoutis, N., Maropoulos, P., Matthews, P., & Lomas, C. (2008). Establishing agile supply networks through competence profiling. International Journal of Computer Integrated Manufacturing, 21(2), 166-173. https://doi.org/10.1080/09511920701607683
• Meta-Design for Agile Concurrent Product Design in the Virtual Enterprise
  
  Lomas, C., & Matthews, P. (2007). Meta-Design for Agile Concurrent Product Design in the Virtual Enterprise. International Journal of Agile Manufacturing., 10(2), 77-87.
• Implementing the Information Pump using Accessible Technology
  
  Matthews, P., & Chesters, P. (2006). Implementing the Information Pump using Accessible Technology. Journal of Engineering Design, 17(6), 563-585. https://doi.org/10.1080/09544820600646629
• Learning inexpensive parametric design models using an augmented genetic programming technique
  
  Matthews, P., Standingford, D., Holden, C., & Wallace, K. (2006). Learning inexpensive parametric design models using an augmented genetic programming technique. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 20(1), 1-18. https://doi.org/10.1017/s089006040606001x
• Foundations of an Agile Design Methodology
  
  Matthews, P., Lomas, C., Armoutis, N., & Maropoulos, P. (2006). Foundations of an Agile Design Methodology. International Journal of Agile Manufacturing., 9(1), 29-38.
• Measuring Design Process Agility for the Single Company Product Development Process
  
  Lomas, C., Wilkinson, J., Maropoulos, P., & Matthews, P. (2006). Measuring Design Process Agility for the Single Company Product Development Process. International Journal of Agile Manufacturing., 9(2), 105-112.
• The introduction of a design heuristics extraction method
  
  Matthews, P., Blessing, L., & Wallace, K. (2002). The introduction of a design heuristics extraction method. Advanced Engineering Informatics, 16(1), 3-19. https://doi.org/10.1016/s1474-0346%2802%2900002-2

Patent

• Method of Design using Genetic Programming
  
  Matthews, P., Standingford, D., & Holden, C. (2003). Method of Design using Genetic Programming (Patent).
• Method of design using genetic programming
  
  Matthews, P., Standingford, D., & Holden, C. (2002). Method of design using genetic programming (Patent).

Report

• High Level Summary of Learning: Domestic Smart Meter Customers
  
  Bulkeley, H., Whitaker, G., Matthews, P., Bell, S., Lyon, S., & Powells, G. (2015). High Level Summary of Learning: Domestic Smart Meter Customers. Northern Powergrid (Northeast) Limited, Northern Powergrid (Yorkshire) Plc, British Gas Trading Limited, EA Technology Limited and the University of Durham.
• High Level Summary of Learning: Heat Pump Customers
  
  Bell, S., Capova, K., Barteczko-Hibbert, C., Matthews, P., Wardle, R., Bulkeley, H., Lyon, S., Judson, E., & Powells, G. (2015). High Level Summary of Learning: Heat Pump Customers. Northern Powergrid (Northeast) Limited, Northern Powergrid (Yorkshire) Plc, British Gas Trading Limited, University of Durham and EA Technology Ltd.
• High Level Summary of Learning: Domestic Smart Meter Customers on Time of Use Tariffs
  
  Bulkeley, H., Matthews, P., Whitaker, G., Bell, S., Wardle, R., Lyon, S., & Powells, G. (2015). High Level Summary of Learning: Domestic Smart Meter Customers on Time of Use Tariffs. Northern Powergrid (Northeast) Limited, Northern Powergrid (Yorkshire) Plc, British Gas Trading Limited, University of Durham and EA Technology Ltd.
• High Level Summary of Learning: Electrical Vehicle Users
  
  Capova, K., Wardle, R., Bell, S., Lyon, S., Bulkeley, H., Matthews, P., & Powells, G. (2015). High Level Summary of Learning: Electrical Vehicle Users. Northern Powergrid (Northeast) Limited, Northern Powergrid (Yorkshire) Plc, British Gas Trading Limited, University of Durham and EA Technology Ltd.
• High Level Summary of Learning: Domestic Solar PV Customers
  
  Bulkeley, H., Whitaker, G., Matthews, P., Bell, S., Lyon, S., & Powells, G. (2015). High Level Summary of Learning: Domestic Solar PV Customers. Northern Powergrid (Northeast) Limited, Northern Powergrid (Yorkshire) Plc, British Gas Trading Limited, EA Technology Limited and the University of Durham.
• Micro-CHP Trial Report
  
  Jones, O., Wardle, R., & Matthews, P. (2014). Micro-CHP Trial Report. Northern Powergrid (Northeast) Limited, Northern Powergrid (Yorkshire) Plc, British Gas Trading Limited, University of Durham and EA Technology Ltd.
• Insight Report: Domestic Time of Use Tariff: A comparison of the time of use tariff trial to the baseline domestic profiles
  
  Whitaker, G., Wardle, R., Barteczko-Hibbert, C., Matthews, P., Bulkeley, H., & Powells, G. (2013). Insight Report: Domestic Time of Use Tariff: A comparison of the time of use tariff trial to the baseline domestic profiles. Northern Powergrid (Northeast) Limited, Northern Powergrid (Yorkshire) Plc, British Gas Trading Limited EA Technology Ltd and the University of Durham.
• Identifying Design Micro-models using Genetic Programming Techniques: A User Manual for the GP-HEM toolbox
  
  Matthews, P. (2003). Identifying Design Micro-models using Genetic Programming Techniques: A User Manual for the GP-HEM toolbox.