Evolutionary Synthesis of HVAC System Configurations: Algorithm Development.

This paper describes the development of an optimization procedure for the synthesis of novel heating, ventilating, and air-conditioning (HVAC) system configurations. Novel HVAC system designs can be synthesized using model-based optimization methods. The optimization problem can be considered as having three sub-optimization problems; the choice of a component set; the design of the topological connections between the components; and the design of a system operating strategy. In an attempt to limit the computational effort required to obtain a design solution, the approach adopted in this research is to solve all three sub-problems simultaneously. Further, the computational effort has been limited by implementing simplified component models and including the system performance evaluation as part of the optimization problem (there being no need in this respect to simulation the system performance). The optimization problem has been solved using a Genetic Algorithm (GA), with data structures and search operators that are specifically developed for the solution of HVAC system optimization problems (in some instances, certain of the novel operators may also be used in other topological optimization problems. The performance of the algorithm, and various search operators has been examined for a two-zone optimization problem (the objective of the optimization being to find a system design that minimizes the system energy use). In particular, the performance of the algorithm in finding feasible system designs has been examined. It was concluded that the search was unreliable when the component set was optimized, but if the component set was fixed as a boundary condition on the search, then the algorithm had an 81% probability of finding a feasible system design. The optimality of the solutions is not examined in this paper, but is described in an associated publication. It was concluded that, given a candidate set of system components, the algorithm described here provides an effective tool for exploring the novel design of HVAC systems. (c) HVAC & R journa

Estimating the air change rates in dwellings using a heat balance approach

Infiltration and ventilation rates in domestic buildings vary with construction type, weather conditions and the operation of openings in the fabric. Generating good estimates of ventilation is important for modelling, simulation and performance assessment as it has a significant impact on energy consumption. Physical tests can be applied to estimate leakage, but this is cumbersome and impractical to apply in most cases. This paper applies a heat balance approach to energy monitoring data to estimate a parameter that describes the combined ventilation and infiltration rates in real family homes. These estimates are compared with published values and a model is presented that describes the air change rate as a function of user behaviour (control of openings) and varying wind speed. The paper demonstrates that it is possible to estimate plausible air change rates from such data

A Comparison of Approaches to Stepwise Regression Analysis for Variables Sensitivity Measurements Used with a Multi-Objective Optimization Problem

Global sensitivity analysis can be used to identify and rank variables importance (sensitivities) for design objectives and constraints, where the solution space is sampled and a linear regression model is normally adopted in the stepwise manner. The relative importance of variables can be examined by three indicators: the order of variables entry into the linear regression model; the absolute values of the standardized regression coefficients or their rank transformation coefficients; and the size of the R2 changes (coefficient of determination) attributable to additional variables at each step. However, the robustness of the linear regression model constructed from a stepwise regression is related to the choice of procedure options, e.g. the set of samples and data formulation. Different procedure options could lead to different linear regression models, and therefore influence the indication of variables global sensitivities. Thus, this paper investigates the extent to which the procedure options of a stepwise regression can influence the indication of variables global sensitivities, measured by three different sensitivity indicators, for energy demand, capital costs and solution infeasibility, when using both the randomly generated samples and the biased solutions obtained at the start of a multi-objective optimization process (based on NSGA-II). It concludes that the most important variables are always ranked on the top no matter the choice of procedure options, but it is better to adopt both the entry-orders of variables and their standardized (rank) regression coefficients or the contributions to R2 changes, to provide robust orderings of variables importance, for design objectives and constraints. Moreover, when the sample size is smaller, re-generated separate set of samples for sensitivity analysis can avoid misleading variables importance, especially for the variables ranked in the middle. Finally, to improve computational efficiency, this paper concludes that the first 100 solutions obtained from a multi-objective optimization can be used to perform global sensitivity analysis, to identify the important variables for design objectives

Applying Global And Local SA In Identification Of Variables Importance With The Use Of Multi-Objective Optimization

Methods for global and local Sensitivity analysis are designed to identify and rank variables importance for each design objective and constraint. This paper investigates the application of local sensitivity analysis to a set of Pareto optimum solutions resulting from the multi-objective minimization of energy use and capital cost, with occupant thermal comfort acting as a constraint. It is concluded that the local sensitivities vary along the trade-off and that these sensitivities are different to the global sensitivities. Different sensitivity behaviour is also observed both along the Pareto trade-off and between variables

Energy Modelling and Calibration of Building Simulations: A Case Study of a Domestic Building with Natural Ventilation

[EN] In this paper, the building energy performance modelling tools TRNSYS (TRaNsient SYstem Simulation program) and TRNFlow (TRaNsient Flow) have been used to obtain the energy demand of a domestic building that includes the air infiltration rate and the effect of natural ventilation by using window operation data. An initial model has been fitted to monitoring data from the case study, building over a period when there were no heat gains in the building in order to obtain the building infiltration air change rate. After this calibration, a constant air-change rate model was established alongside two further models developed in the calibration process. Air change rate has been explored in order to determine air infiltrations caused by natural ventilation due to windows being opened. These results were compared to estimates gained through a previously published method and were found to be in good agreement. The main conclusion from the work was that the modelling ventilation rate in naturally ventilated residential buildings using TRNSYS and TRNSFlow can improve the simulation-based energy assessment.Aparicio-Fernández, C.; Vivancos, J.; Cosar-Jorda, P.; Buswell, RA. (2019). Energy Modelling and Calibration of Building Simulations: A Case Study of a Domestic Building with Natural Ventilation. Energies. 12(17):1-13. https://doi.org/10.3390/en12173360S1131217Grygierek, K., & Ferdyn-Grygierek, J. (2018). Multi-Objective Optimization of the Envelope of Building with Natural Ventilation. Energies, 11(6), 1383. doi:10.3390/en11061383Moran, P., Goggins, J., & Hajdukiewicz, M. (2017). Super-insulate or use renewable technology? Life cycle cost, energy and global warming potential analysis of nearly zero energy buildings (NZEB) in a temperate oceanic climate. Energy and Buildings, 139, 590-607. doi:10.1016/j.enbuild.2017.01.029Allouhi, A., El Fouih, Y., Kousksou, T., Jamil, A., Zeraouli, Y., & Mourad, Y. (2015). Energy consumption and efficiency in buildings: current status and future trends. Journal of Cleaner Production, 109, 118-130. doi:10.1016/j.jclepro.2015.05.139Cosar-Jorda, P., Buswell, R. A., & Mitchell, V. A. (2018). Determining of the role of ventilation in residential energy demand reduction using a heat-balance approach. Building and Environment, 144, 508-518. doi:10.1016/j.buildenv.2018.08.053Feijó-Muñoz, J., Poza-Casado, I., González-Lezcano, R. A., Pardal, C., Echarri, V., Assiego De Larriva, R., … Meiss, A. (2018). Methodology for the Study of the Envelope Airtightness of Residential Buildings in Spain: A Case Study. Energies, 11(4), 704. doi:10.3390/en11040704Domínguez-Amarillo, S., Fernández-Agüera, J., Campano, M. Á., & Acosta, I. (2019). Effect of Airtightness on Thermal Loads in Legacy Low-Income Housing. Energies, 12(9), 1677. doi:10.3390/en12091677Cheng, P. L., & Li, X. (2018). Air infiltration rates in the bedrooms of 202 residences and estimated parametric infiltration rate distribution in Guangzhou, China. Energy and Buildings, 164, 219-225. doi:10.1016/j.enbuild.2017.12.062Hou, J., Zhang, Y., Sun, Y., Wang, P., Zhang, Q., Kong, X., & Sundell, J. (2018). Air change rates at night in northeast Chinese homes. Building and Environment, 132, 273-281. doi:10.1016/j.buildenv.2018.01.030Zhai, Z. (John), Mankibi, M. E., & Zoubir, A. (2015). Review of Natural Ventilation Models. Energy Procedia, 78, 2700-2705. doi:10.1016/j.egypro.2015.11.355Han, G., Srebric, J., & Enache-Pommer, E. (2015). Different modeling strategies of infiltration rates for an office building to improve accuracy of building energy simulations. Energy and Buildings, 86, 288-295. doi:10.1016/j.enbuild.2014.10.028Laverge, J., & Janssens, A. (2013). Optimization of design flow rates and component sizing for residential ventilation. Building and Environment, 65, 81-89. doi:10.1016/j.buildenv.2013.03.019Bhandari, M., Hun, D., Shrestha, S., Pallin, S., & Lapsa, M. (2018). A Simplified Methodology to Estimate Energy Savings in Commercial Buildings from Improvements in Airtightness. Energies, 11(12), 3322. doi:10.3390/en11123322Pisello, A. L., Castaldo, V. L., Taylor, J. E., & Cotana, F. (2016). The impact of natural ventilation on building energy requirement at inter-building scale. Energy and Buildings, 127, 870-883. doi:10.1016/j.enbuild.2016.06.023Tronchin, L., Fabbri, K., & Bertolli, C. (2018). Controlled Mechanical Ventilation in Buildings: A Comparison between Energy Use and Primary Energy among Twenty Different Devices. Energies, 11(8), 2123. doi:10.3390/en11082123Ashdown, M. M. A., Crawley, J., Biddulph, P., Wingfield, J., Lowe, R., & Elwell, C. A. (2019). Characterising the airtightness of dwellings. International Journal of Building Pathology and Adaptation, 38(1), 89-106. doi:10.1108/ijbpa-02-2019-0024Crawley, J., Wingfield, J., & Elwell, C. (2018). The relationship between airtightness and ventilation in new UK dwellings. Building Services Engineering Research and Technology, 40(3), 274-289. doi:10.1177/0143624418822199Jones, B., Das, P., Chalabi, Z., Davies, M., Hamilton, I., Lowe, R., … Taylor, J. (2015). Assessing uncertainty in housing stock infiltration rates and associated heat loss: English and UK case studies. Building and Environment, 92, 644-656. doi:10.1016/j.buildenv.2015.05.033Schulze, T., & Eicker, U. (2013). Controlled natural ventilation for energy efficient buildings. Energy and Buildings, 56, 221-232. doi:10.1016/j.enbuild.2012.07.044Stavridou, A. D., & Prinos, P. E. (2017). Unsteady CFD Simulation in a Naturally Ventilated Room with a Localized Heat Source. Procedia Environmental Sciences, 38, 322-330. doi:10.1016/j.proenv.2017.03.087LEEDR Project Home Energy Datasethttps://repository.lboro.ac.uk/articles/LEEDR_project_home_energy_dataset/6176450Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)http://catalogue.ceda.ac.uk/uuid/220a65615218d5c9cc9e4785a3234bd0Buswell, R., Webb, L., Mitchell, V., & Leder Mackley, K. (2016). Multidisciplinary research: should effort be the measure of success? Building Research & Information, 45(5), 539-555. doi:10.1080/09613218.2016.1194601National Grid UKhttps://www.nationalgrid.com/uk/gas/market-operations-and-data/calorific-value-cvHome Heating Guide: Boiler Efficiency Tableshttps://www.homeheatingguide.co.uk/efficiency-tablesRuiz, G., & Bandera, C. (2017). Validation of Calibrated Energy Models: Common Errors. Energies, 10(10), 1587. doi:10.3390/en10101587Hong, T., Piette, M. A., Chen, Y., Lee, S. H., Taylor-Lange, S. C., Zhang, R., … Price, P. (2015). Commercial Building Energy Saver: An energy retrofit analysis toolkit. Applied Energy, 159, 298-309. doi:10.1016/j.apenergy.2015.09.002Nasir, Z. A., & Colbeck, I. (2013). Particulate pollution in different housing types in a UK suburban location. Science of The Total Environment, 445-446, 165-176. doi:10.1016/j.scitotenv.2012.12.042Dimitroulopoulou, C. (2012). Ventilation in European dwellings: A review. Building and Environment, 47, 109-125. doi:10.1016/j.buildenv.2011.07.01

3D printing using concrete extrusion: A roadmap for research

Large-scale additive manufacturing processes for construction utilise computer-controlled placement of extruded cement-based mortar to create physical objects layer-by-layer. Demonstrated applications include component manufacture and placement of in-situ walls for buildings. These applications vary the constraints on design parameters and present different technical issues for the production process. In this paper, published and new work are utilised to explore the relationship between fresh and hardened paste, mortar, and concrete material properties and how they influence the geometry of the created object. Findings are classified by construction application to create a matrix of issues that identifies the spectrum of future research exploration in this emerging field

Exploring rapid prototyping techniques for validating numerical models of naturally ventilated buildings

An alternative to using numerical simulation to model ventilation performance is to model internal air flows using water-based experimental models. However, these can be time consuming and the manual nature of model assembly means that exploring detail and design variations is often prohibitively expensive. Additive, or Rapid manufacturing processes can build physical models directly from 3D-CAD data and is widely used in product development within the aero-automotive and consumer goods industries. This paper describes ongoing work exploring the application of such techniques for the production of physical models which can be used in their own right in water-based testing or for Computational fluid dynamics (CFD) validation. The findings presented here suggest such techniques present a worthwhile alternative to traditional model fabrication methods

The application of quality control charts for identifying changes in time-series home energy data

[EN] Energy consumption in the home is heavily influenced by the occupants and the routines they adopt. Although these routines tend to be regarded as somewhat static in nature, more recent evidence from the social sciences suggests that patterns of consumption are actually more fluid and constantly evolve to accommodate the contingencies of everyday living. This makes detecting changes in patterns of activity and their impact on energy consumption difficult, particularly when these patterns are often invisible to the householder to begin with. Being able to identify when a change occurs, therefore, could be a powerful tool to establish the context of change and so to determine more appropriate corrective action to curb waste and create opportunities for greater flexibility in consumption. The growing adoption of smart meters and home energy monitoring provide a platform for numerical approaches, yet there is little work reported in the literature and none that have attempted to evaluate effectiveness of such methods applied to detect changes in behaviour using field monitoring data from family homes. This paper reports on the application of a Change Point Detection method based on statistical quality control charts applied to identify changes in activities a family home using typical monitoring data. The approach was found to be very effective, identifying 78% of the changes that occurred over a two-year period and hence the outlook for such methods is promising. The findings suggest that such techniques could significantly improve the quality of information provided in energy feedback and so could play a significant role in the pursuit of more efficient energy use in the home by adding value to monitoring systems and services.The data used to underpin this has been produced under the LEEDR: Low Effort Energy Demand Reduction Project based at Loughborough University, UK (EPSRC Grant Number EP/I00 0267/1).Vivancos, J.; Buswell, RA.; Cosar-Jorda, P.; Aparicio Fernandez, CS. (2020). The application of quality control charts for identifying changes in time-series home energy data. Energy and Buildings. 215:1-11. https://doi.org/10.1016/j.enbuild.2020.109841S111215J.E. Morrissey, S. Axon, R. Aiesha, J. Hillman, A. Revez, B. Lennon, Identification and behaviour change initiatives, 2016.Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25(3), 273-291. doi:10.1016/j.jenvp.2005.08.002Shove, E. (2003). Converging Conventions of Comfort, Cleanliness and Convenience. Journal of Consumer Policy, 26(4), 395-418. doi:10.1023/a:1026362829781Steemers, K., & Yun, G. Y. (2009). Household energy consumption: a study of the role of occupants. Building Research & Information, 37(5-6), 625-637. doi:10.1080/09613210903186661Cosar-Jorda, P., Buswell, R. A., & Mitchell, V. A. (2018). Determining of the role of ventilation in residential energy demand reduction using a heat-balance approach. Building and Environment, 144, 508-518. doi:10.1016/j.buildenv.2018.08.053Shipworth, M., Firth, S. K., Gentry, M. I., Wright, A. J., Shipworth, D. T., & Lomas, K. J. (2010). Central heating thermostat settings and timing: building demographics. Building Research & Information, 38(1), 50-69. doi:10.1080/09613210903263007Buswell, R., Webb, L., Mitchell, V., & Leder Mackley, K. (2016). Multidisciplinary research: should effort be the measure of success? Building Research & Information, 45(5), 539-555. doi:10.1080/09613218.2016.1194601Pink, S., Mackley, K. L., & Moroşanu, R. (2013). Hanging out at home: Laundry as a thread and texture of everyday life. International Journal of Cultural Studies, 18(2), 209-224. doi:10.1177/1367877913508461Yan, D., O’Brien, W., Hong, T., Feng, X., Burak Gunay, H., Tahmasebi, F., & Mahdavi, A. (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107, 264-278. doi:10.1016/j.enbuild.2015.08.032Hargreaves, T. (2011). Practice-ing behaviour change: Applying social practice theory to pro-environmental behaviour change. Journal of Consumer Culture, 11(1), 79-99. doi:10.1177/1469540510390500Aminikhanghahi, S., & Cook, D. J. (2016). A survey of methods for time series change point detection. Knowledge and Information Systems, 51(2), 339-367. doi:10.1007/s10115-016-0987-zStrengers, Y. (2011). Negotiating everyday life: The role of energy and water consumption feedback. Journal of Consumer Culture, 11(3), 319-338. doi:10.1177/1469540511417994HM Government, Industrial Strategy: Building a Britain fit for the future, (2017). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/664563/industrial-strategy-white-paper-web-ready-version.pdf?_ga=2.176869477.131331705.1566905390-1346621436.1566905390.Darby, S. J. (2017). Smart electric storage heating and potential for residential demand response. Energy Efficiency, 11(1), 67-77. doi:10.1007/s12053-017-9550-3Department for Business Energy & Industrial Strategy, Energy consumption in the UK, 2016.Perez, K. X., Cetin, K., Baldea, M., & Edgar, T. F. (2017). Development and analysis of residential change-point models from smart meter data. Energy and Buildings, 139, 351-359. doi:10.1016/j.enbuild.2016.12.084Frederiks, E. R., Stenner, K., Hobman, E. V., & Fischle, M. (2016). Evaluating energy behavior change programs using randomized controlled trials: Best practice guidelines for policymakers. Energy Research & Social Science, 22, 147-164. doi:10.1016/j.erss.2016.08.020Gynther, L., Mikkonen, I., & Smits, A. (2011). Evaluation of European energy behavioural change programmes. Energy Efficiency, 5(1), 67-82. doi:10.1007/s12053-011-9115-9Behavioural Insights Team, Behaviour Change and Energy Use, Cabinet Off. London(Available at)/Http//Www.Cabinetoffice.Gov.Uk/Resourcelibrary/Behaviour-Change-and-Energy-UseS. (2011).Pink, S., & Mackley, K. L. (2012). Video and a Sense of the Invisible: Approaching Domestic Energy Consumption through the Sensory Home. Sociological Research Online, 17(1), 87-105. doi:10.5153/sro.2583Belaïd, F., Bakaloglou, S., & Roubaud, D. (2018). Direct rebound effect of residential gas demand: Empirical evidence from France. Energy Policy, 115, 23-31. doi:10.1016/j.enpol.2017.12.040Wilson, C., Hargreaves, T., & Hauxwell-Baldwin, R. (2017). Benefits and risks of smart home technologies. Energy Policy, 103, 72-83. doi:10.1016/j.enpol.2016.12.047Mogles, N., Walker, I., Ramallo-González, A. P., Lee, J., Natarajan, S., Padget, J., … Coley, D. (2017). How smart do smart meters need to be? Building and Environment, 125, 439-450. doi:10.1016/j.buildenv.2017.09.008Hargreaves, T., Nye, M., & Burgess, J. (2010). Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors. Energy Policy, 38(10), 6111-6119. doi:10.1016/j.enpol.2010.05.068Darby, S. J. (2017). Smart technology in the home: time for more clarity. Building Research & Information, 46(1), 140-147. doi:10.1080/09613218.2017.1301707Lynham, J., Nitta, K., Saijo, T., & Tarui, N. (2016). Why does real-time information reduce energy consumption? Energy Economics, 54, 173-181. doi:10.1016/j.eneco.2015.11.007Owens, S., & Driffill, L. (2008). How to change attitudes and behaviours in the context of energy. Energy Policy, 36(12), 4412-4418. doi:10.1016/j.enpol.2008.09.031Seem, J. E. (2007). Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39(1), 52-58. doi:10.1016/j.enbuild.2006.03.033Richardson, I., Thomson, M., Infield, D., & Clifford, C. (2010). Domestic electricity use: A high-resolution energy demand model. Energy and Buildings, 42(10), 1878-1887. doi:10.1016/j.enbuild.2010.05.023Meyers, R. J., Williams, E. D., & Matthews, H. S. (2010). Scoping the potential of monitoring and control technologies to reduce energy use in homes. Energy and Buildings, 42(5), 563-569. doi:10.1016/j.enbuild.2009.10.026Weiss, M., Patel, M. K., Junginger, M., & Blok, K. (2010). Analyzing price and efficiency dynamics of large appliances with the experience curve approach. Energy Policy, 38(2), 770-783. doi:10.1016/j.enpol.2009.10.022Yu, Z., Haghighat, F., Fung, B. C. M., & Yoshino, H. (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42(10), 1637-1646. doi:10.1016/j.enbuild.2010.04.006Richardson, I., Thomson, M., & Infield, D. (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40(8), 1560-1566. doi:10.1016/j.enbuild.2008.02.006Haldi, F., & Robinson, D. (2008). On the behaviour and adaptation of office occupants. Building and Environment, 43(12), 2163-2177. doi:10.1016/j.buildenv.2008.01.003Bacher, P., & Madsen, H. (2011). Identifying suitable models for the heat dynamics of buildings. Energy and Buildings, 43(7), 1511-1522. doi:10.1016/j.enbuild.2011.02.005Santamouris, M., Mihalakakou, G., Patargias, P., Gaitani, N., Sfakianaki, K., Papaglastra, M., … Zerefos, S. (2007). Using intelligent clustering techniques to classify the energy performance of school buildings. Energy and Buildings, 39(1), 45-51. doi:10.1016/j.enbuild.2006.04.018Van Raaij, W. F., & Verhallen, T. M. M. (1983). Patterns of residential energy behavior. Journal of Economic Psychology, 4(1-2), 85-106. doi:10.1016/0167-4870(83)90047-8Papakostas, K. T., & Sotiropoulos, B. A. (1997). Occupational and energy behaviour patterns in Greek residences. Energy and Buildings, 26(2), 207-213. doi:10.1016/s0378-7788(97)00002-9Aminikhanghahi, S., & Cook, D. J. (2019). Enhancing activity recognition using CPD-based activity segmentation. Pervasive and Mobile Computing, 53, 75-89. doi:10.1016/j.pmcj.2019.01.004Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 10, 138-154. doi:10.1016/j.pmcj.2012.07.003Wei, S., Jones, R., & de Wilde, P. (2014). Driving factors for occupant-controlled space heating in residential buildings. Energy and Buildings, 70, 36-44. doi:10.1016/j.enbuild.2013.11.001R. Buswell, L. Webb, P. Cosar-Jorda, D. Marini, S. Brownlee, M. Thomson, S.-.H. Yang, R. Kalawsky, LEEDR project home energy dataset, (2018). doi:10.17028/rd.lboro.6176450.v1.Kulinskaya, E., & Koricheva, J. (2010). Use of quality control charts for detection of outliers and temporal trends in cumulative meta-analysis. Research Synthesis Methods, 1(3-4), 297-307. doi:10.1002/jrsm.29Hyndman, R. J., & Khandakar, Y. (2008). Automatic Time Series Forecasting: TheforecastPackage forR. Journal of Statistical Software, 27(3). doi:10.18637/jss.v027.i03D. Montgomery, Introduction to statistical quality control, 2009. doi:10.1002/1521-3773(20010316)40:63.3.CO;2-C.Barbeito, I., Zaragoza, S., Tarrío-Saavedra, J., & Naya, S. (2017). Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data. Applied Energy, 190, 1-17. doi:10.1016/j.apenergy.2016.12.100Haines, V., & Mitchell, V. (2014). A persona-based approach to domestic energy retrofit. Building Research & Information, 42(4), 462-476. doi:10.1080/09613218.2014.893161Eon S.E., Annual report2017, n.d.https://www.eon.com/content/dam/eon/eon-com/investors/annual-report/EON_GB17_EN.pdf(accessed December 21, 2018).Paradiso, F., Paganelli, F., Giuli, D., & Capobianco, S. (2016). Context-Based Energy Disaggregation in Smart Homes. Future Internet, 8(1), 4. doi:10.3390/fi801000