AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

A framework for building energy management system with residence mounted photovoltaic

C Chellaswamy( ), R Ganesh Babu, A Vanathi
Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, India
Department of Electronics and Communication Engineering, SRM TRP Engineering College, Tiruchirappalli, India
Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India
Show Author Information

Abstract

Efficient utilization of a residential photovoltaic (PV) array with grid connection is difficult due to power fluctuation and geographical dispersion. Reliable energy management and control system are required for overcoming these obstacles. This study provides a new residential energy management system (REMS) based on the convolution neural network (CNN) including PV array environment. The CNN is used in the estimation of the nonlinear relationship between the residence PV array power and meteorological datasets. REMS has three main stages for the energy management such as forecasting, scheduling, and real functioning. A short term forecasting strategy has been performed in the forecasting stage based on the PV power and the residential load. A coordinated scheduling has been utilized for minimizing the functioning cost. A real-time predictive strategy has been used in the actual functioning stage to minimize the difference between the actual and scheduled power consumption of the building. The proposed approach has been evaluated based on real-time power and meteorological data sets.

Keywords

convolution neural networkbuilding energy managementphotovoltaiccoordinated scheduling

References

 
Abawi Y, Rennhofer M, Berger K, et al. (2016). Comparison of theoretical and real energy yield of direct DC-power usage of a Photovoltaic Façade system. Renewable Energy, 89: 616-626.
Crossref Google Scholar
 
Adika CO, Wang L (2014). Smart charging and appliance scheduling approaches to demand side management. International Journal of Electrical Power & Energy Systems, 57: 232-240.
Crossref Google Scholar
 
Aelenei L, Pereira R, Gonçalves H, et al. (2014). Thermal performance of a hybrid BIPV-PCM: modeling, design and experimental investigation. Energy Procedia, 48: 474-483.
Crossref Google Scholar
 
Aguacil S, Lufkin S, Rey E (2019). Active surfaces selection method for building-integrated photovoltaics (BIPV) in renovation projects based on self-consumption and self-sufficiency. Energy and Buildings, 193: 15-28.
Crossref Google Scholar
 
Assoa YB, Gaillard L, Ménézo C, et al. (2018). Dynamic prediction of a building integrated photovoltaic system thermal behaviour. Applied Energy, 214: 73-82.
Crossref Google Scholar
 
Atthajariyakul S, Leephakpreeda T (2005). Neural computing thermal comfort index for HVAC systems. Energy Conversion and Management, 46: 2553-2565.
Crossref Google Scholar
 
Beaudin M, Zareipour H (2015). Home energy management systems: A review of modelling and complexity. Renewable and Sustainable Energy Reviews, 45: 318-335.
Crossref Google Scholar
 
Bocquet A, Michiorri A, Bossavy A, et al. (2016). Assessment of probabilistic PV production forecasts performance in an operational context. In: Proceedings of the 6th International Workshop on Integration of Solar Power into Power Systems, Vienna, Austria.
 
Buratti C, Ricciardi P, Vergoni M (2013). HVAC systems testing and check: A simplified model to predict thermal comfort conditions in moderate environments. Applied Energy, 104: 117-127.
Crossref Google Scholar
 
CMDC (2018). Share data: NWP. China Meteorological Data Network. Available at http://data.cma.cn/site/index.html. Accessed Feb 2018.
 
Couty P, Lalou MJ, Cuony P, et al. (2017). Positive energy building with PV facade production and electrical storage designed by the Swiss team for the US Department of Energy Solar Decathlon 2017. Energy Procedia, 122: 919-924.
Crossref Google Scholar
 
Cui H, Zhou K (2018). Industrial power load scheduling considering demand response. Journal of Cleaner Production, 204: 447-460.
Crossref Google Scholar
 
del Valle Y, Venayagamoorthy GK, Mohagheghi S, et al. (2008). Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 12: 171-195.
Crossref Google Scholar
 
ECMWF (2017). Public Datasets: ERA-Interim and CAMS Near-Real-Time. European Centre for Medium-Range Weather Forecasts. Available at http://apps.ecmwf.int/datasets/. Accessed Oct 2017.
 
EEA (2017). AQ e-Reporting. European Environment Agency. Available at https://www.eea.europa.eu/data-and-maps/data/aqereporting-2. Accessed Oct 2017.
Crossref
 
ELIA (2017). Solar-PV Power Generation Data. Elia Group. Available at http://www.elia.be/en/grid-data/power-generation. Accessed Oct 2017
 
Faria P, Soares J, Vale Z, et al. (2013). Modified particle swarm optimization applied to integrated demand response and DG resources scheduling. IEEE Transactions on Smart Grid, 4: 606-616.
Crossref Google Scholar
 
Ferreira PM, Ruano AE, Silva S, et al. (2012a). Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy and Buildings, 55: 238-251.
Crossref Google Scholar
 
Ferreira PM, Silva SM, Ruano AE, et al. (2012b). Neural network PMV estimation for model-based predictive control HVAC systems. In: Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia.
Crossref
 
Gensler A, Henze J, Sick B, et al. (2016). Deep learning for solar power forecasting—An approach using Auto-Encoder and LSTM Neural Networks. In: Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
Crossref
 
Gerossier A, Girard R, Kariniotakis G, et al. (2017). Probabilistic day-ahead forecasting of household electricity demand. In: Proceedings of the 24th International Conference on Electricity Distribution (CIRED 2017), Glasgow, UK.
Crossref
 
Ghosh A, Norton B (2018). Advances in switchable and highly insulating autonomous (self-powered) glazing systems for adaptive low energy buildings. Renewable Energy, 126: 1003-1031.
Crossref Google Scholar
 
Gong X, Michel P, Cantin R (2019). Multiple-criteria decision analysis of BIM influences in building energy management. Building Simulation, 12: 641-652.
Crossref Google Scholar
 
Hong T, Fan S (2016). Probabilistic electric load forecasting: a tutorial review. International Journal of Forecasting, 32: 914-938.
Crossref Google Scholar
 
Ioffe S, Szegedy C (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, Lille, France.
 
Irshad K, Habib K, Algarni S, et al. (2019). Sizing and life-cycle assessment of building integrated thermoelectric air cooling and photovoltaic wall system. Applied Thermal Engineering, 154: 302-314.
Crossref Google Scholar
 
Iwafune Y, Ikegami T, Silva Fonseca JGD Jr, et al. (2015). Cooperative home energy management using batteries for a photovoltaic system considering the diversity of households. Energy Conversion and Management, 96: 322-329.
Crossref Google Scholar
 
Jacob L, Jeyakrishanan V, Sengottuvelan P (2014). Resource scheduling in cloud using bacterial foraging optimization algorithm. International Journal of Computer Applications, 92: 14-20.
Crossref Google Scholar
 
Jelle BP, Breivik C, Røkenes HD (2012). Building integrated photovoltaic products: A state-of-the-art review and future research opportunities. Solar Energy Materials and Solar Cells, 100: 69-96.
Crossref Google Scholar
 
Kaur J, Bala A (2019). A hybrid energy management approach for home appliances using climatic forecasting. Building Simulation, 12: 1033-1045.
Crossref Google Scholar
 
Krizhevsky A, Sutskever I, Hinton GE (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60: 84-90.
Crossref Google Scholar
 
Ku K, Jeong S (2018). Building electric energy prediction modeling for BEMS using easily obtainable weather factors with Kriging model and data mining. Building Simulation, 11: 739-751.
Crossref Google Scholar
 
LeCun Y, Bengio Y, Hinton G (2015). Deep learning. Nature, 521: 436-444.
Crossref Google Scholar
 
Li J, Dang J, Bu F, et al. (2014). Analysis and improvement of the bacterial foraging optimization algorithm. Journal of Computing Science and Engineering, 8: 1-10.
Crossref Google Scholar
 
Liu Y, Yuen C, Huang S, et al. (2014). Peak-to-average ratio constrained demand-side management with consumer’s preference in residential smart grid. IEEE Journal of Selected Topics in Signal Processing, 8: 1084-1097.
Crossref Google Scholar
 
Logenthiran T, Srinivasan D, Shun T (2012). Demand side management in smart grid using heuristic optimization. IEEE Transactions on Smart Grid, 3: 1244-1252.
Crossref Google Scholar
 
Luo F, Dong Z, Meng K, et al. (2017a). An operational planning framework for large-scale thermostatically controlled load dispatch. IEEE Transactions on Industrial Informatics, 13: 217-227.
Crossref Google Scholar
 
Luo F, Ranzi G, Liang G, et al. (2017b). Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm. In: Proceedings of IEEE Power & Energy Society General Meeting, Chicago, USA.
Crossref
 
Ma H, Du N, Yu S, et al. (2017). Analysis of typical public building energy consumption in Northern China. Energy and Buildings, 136: 139-150.
Crossref Google Scholar
 
Mahmood D, Javaid N, Alrajeh N, et al. (2016). Realistic scheduling mechanism for smart homes. Energies, 9: 202.
Crossref Google Scholar
 
Martinez-Pabon M, Eveleigh T, Tanju B (2018). Optimizing residential energy management using an autonomous scheduler system. Expert Systems with Applications, 96: 373-387.
Crossref Google Scholar
 
Miao H, Huang X, Chen G (2012). A genetic evolutionary task scheduling method for energy efficiency in smart homes. International Journal of Electrical Engineering, 7(5):5897-5904.
Google Scholar
 
Morel N, Bauer M, El-Khoury M, et al. (2001). Neurobat, A predictive and adaptive heating control system using artificial neural networks. International Journal of Solar Energy, 21: 161-201.
Crossref Google Scholar
 
Nguyen DT, Nguyen HT, Le LB, (2014). Coordinated dispatch of renewable energy sources and HVAC load using stochastic programming. In: Proceedings of International Conference on Smart Grid Communications, Venice, Italy.
Crossref
 
Oladeji O, Olakanmi O (2014). A genetic algorithm approach to energy consumption scheduling under demand response. In: Proceedings of the 6th International Conference on Adaptive Science and Technology (ICAST).
Crossref
 
Petri I, Li H, Rezgui Y, et al. (2014). A modular optimisation model for reducing energy consumption in large scale building facilities. Renewable and Sustainable Energy Reviews, 38: 990-1002.
Crossref Google Scholar
 
Pipattanasomporn M, Kuzlu M, Rahman S (2012). An algorithm for intelligent home energy management and demand response analysis. IEEE Transactions on Smart Grid, 3: 2166-2173.
Crossref Google Scholar
 
Qayyum FA, Naeem M, Khwaja AS, et al. (2015). Appliance scheduling optimization in smart home networks. IEEE Access, 3: 2176-2190.
Crossref Google Scholar
 
Raj JS, Priya SD (2012). Contribution of BFO in grid scheduling. In: Proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India.
Crossref
 
Ramanathan B, Vittal V (2008). A framework for evaluation of advanced direct load control with minimum disruption. IEEE Transactions on Power Systems, 23: 1681-1688.
Crossref Google Scholar
 
Salinas S, Li M, Li P (2013). Multi-objective optimal energy consumption scheduling in smart grids. IEEE Transactions on Smart Grid, 4: 341-348.
Crossref Google Scholar
 
Schmidhuber J (2015). Deep learning in neural networks: an overview. Neural Networks, 61: 85-117.
Crossref Google Scholar
 
Shahzad S, Brennan J, Theodossopoulos D, et al. (2018). Does a neutral thermal sensation determine thermal comfort? Building Services Engineering Research and Technology, 39: 183-195.
Crossref Google Scholar
 
Shi J, Lee WJ, Liu Y, et al. (2012). Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Transactions on Industry Applications, 48: 1064-1069.
Crossref Google Scholar
 
Shirazi E, Jadid S (2015). Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy and Buildings, 93: 40-49.
Crossref Google Scholar
 
Smart Grid (2017). Smart City. Available at http://www.industry.gov.au/ENERGY/PROGRAMMES/SMARTGRID%20SMARTCITY/Pages/default.aspx.
 
Ten V, Yessenbayev Z, Shamshimov A, et al. (2015). Optimized small-scaled hybrid energy management of a smart housebased on genetic algorithm. In: Proceedings of the 14th IEEE International Conference on Machine Learning and Applications (ICMLA).
Crossref
 
Thapa S, Bansal AK, Panda GK (2018). Thermal comfort in naturally ventilated office buildings in cold and cloudy climate of Darjeeling, India—An adaptive approach. Energy and Buildings, 160: 44-60.
Crossref Google Scholar
 
Vardakas JS, Zorba N, Verikoukis CV (2016). Power demand control scenarios for smart grid applications with finite number of appliances. Applied Energy, 162: 83-98.
Crossref Google Scholar
 
Wang K, Shao Y, Shu L, et al. (2015). LDPA: A local data processing architecture in ambient assisted living communications. IEEE Communications Magazine, 53: 56-63.
Crossref Google Scholar
 
Wang K, Gu L, He X, et al. (2017a). Distributed energy management for vehicle-to-grid networks. IEEE Network, 31: 22-28.
Crossref Google Scholar
 
Wang K, Li H, Feng Y, Tian G (2017b). Big data analytics for system stability evaluation strategy in the energy Internet. IEEE Transactions on Industrial Informatics, 13: 1969-1978.
Crossref Google Scholar
 
Xu Y, Zhang J, Wang W, et al. (2011). Energy router: Architectures and functionalities toward energy Internet. In: Proceedings of IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium.
Crossref
 
Yang HT, Huang C, Huang YC, et al. (2014). A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output. IEEE Transactions on Sustainable Energy, 5: 917-926.
Crossref Google Scholar
 
Yao R, Li B, Liu J (2009). A theoretical adaptive model of thermal comfort—Adaptive Predicted Mean Vote (aPMV). Building and Environment, 44: 2089-2096.
Crossref Google Scholar
 
Yassin MAM, Kolhe M, Sharma A, et al. (2017). Battery capacity estimation for building integrated photovoltaic system: design study for different geographical location(s). Energy Procedia, 142: 3433-3439.
Crossref Google Scholar
 
Yoon S, Yu Y, Wang J, et al. (2019). Impacts of HVACR temperature sensor offsets on building energy performance and occupant thermal comfort. Building Simulation, 12: 259-271.
Crossref Google Scholar
 
Yuce B, Li H, Rezgui Y, et al. (2014). Utilizing artificial neural network to predict energy consumption and thermal comfort level: An indoor swimming pool case study. Energy and Buildings, 80: 45-56.
Crossref Google Scholar
 
Zhan S, Chong A, Lasternas B (2021). Automated recognition and mapping of building management system (BMS) data points for building energy modeling (BEM). Building Simulation, 14: 43-52.
Crossref Google Scholar
 
Zhang Y, Yang S, Guo Z, et al. (2019). Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm. Atmospheric and Oceanic Science Letters, 12: 107-115.
Crossref Google Scholar
 
Zhang X, He K, Bao Y (2020). Error-feedback stochastic configuration strategy on convolutional neural networks for time series forecasting. arXiv:2002.00717.
Google Scholar
 
Zhou B, Li W, Chan KW, et al. (2016). Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and Sustainable Energy Reviews, 61: 30-40.
Crossref Google Scholar
 
Zuo Y, Tao F, Nee AYC (2018). An Internet of Things and cloud-based approach for energy consumption evaluation and analysis for a product. International Journal of Computer Integrated Manufacturing, 31: 337-348.
Crossref Google Scholar
Building Simulation
Volume 14 Issue 4,
August 2021
Pages 1031-1046
DOI: 10.1007/s12273-020-0735-x
Cite this article:
Chellaswamy C, Ganesh Babu R, Vanathi A. A framework for building energy management system with residence mounted photovoltaic. Building Simulation, 2021, 14(4): 1031-1046. https://doi.org/10.1007/s12273-020-0735-x

635

Views

20

Crossref

17

Web of Science

17

Scopus

4

CSCD

Altmetrics
Received: 21 April 2020
Accepted: 28 September 2020
Published: 05 January 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Return