Activitatea I.1.

Cuprins

  1. Colectarea şi analiza datelor privind energia
  2. Determinarea utilizatorilor semnificativi de energie
  3. Stabilirea influenţei diferitor factori asupra utilizării de energie
  4. Stabilirea liniei de bază şi determinarea indicatorilor de performanţă energetică
  5. Linia de bază
  6. Indicatorii de performanţă energetică IPEn
  7. Identificarea cerinţelor legale şi de altă natură
  8. Identificarea oportunităţilor pentru îmbunătăţire
  9. Stabilirea obiectivelor şi ţintelor energetice
  10. Elaborarea planurilor de acţiuni
  11. Bibliografie
Detalii

Bibliografie

[1] Katsinoulas L., Papoutsidakis M., Tseles D., Smart home applications for energy saving and increased security, International Journal of Computer Applications, vol. 175, no. 8, pp. 38–44, Oct. 2017.

[2] Sami B.S., An intelligent power management investigation for stand-alone hybrid system using short-time energy storage. International Journal of Power Electronics and Drive Systems 8.1, 367, 2017.

[3] Yamagishi H., Microbial contamination and countermeasures in home bathrooms and toilets, Indoor Environment, vol. 22, no. 1, pp. 73–79, 2019.

[4] Kim S., Christiaans H., Baek J.S., Smart Homes as Product-Service Systems: Two Focal Areas for Developing Competitive Smart Home Appliances. Service Science 11.4, 292-310, 2019.

[5] Mehrjerdi H., Peer-to-peer home energy management incorporating hydrogen storage system and solar generating units. Renewable Energy 156, 183-192, 2020.

[6] Yoon J., Deep-learning approach to attack handling of IoT devices using IoT-enabled network services. Internet of Things 11, 100241, 2020.

[7] Kim Junyon, HEMS (home energy management system) base on the IoT smart home. Contemporary Engineering Sciences 9.1 21-28, 2016.

[8] Ikpehai A., Adebisi B., Rabie K.M., Broadband PLC for Clustered Advanced Metering Infrastructure (AMI) Architecture. Energies 9, 569, 2016.

[9] Ahmed M.A., Kang Y.C., Kim, Y.C., Communication Network Architectures for Smart-House with Renewable Energy Resources. Energies 8, 8716–8735, 2015.

[10] Bradac Z., Kaczmarczyk V., Fiedler P., Optimal Scheduling of Domestic Appliances via MILP. Energies 8, 217–232, 2015.

[11] Collotta M., Pau G., A Novel Energy Management Approach for Smart Homes Using Bluetooth Low Energy. IEEE J. Sel. Areas Commun. 33, 2988–2996, 2015.

[12] Pascual J., Sanchis P., Marroyo L., Implementation and Control of a Residential Electrothermal Microgrid Based on Renewable Energies, a Hybrid Storage System and Demand Side Management. Energies 7, 210–237, 2014.

[13] Collotta M., Pau G., A Solution Based on Bluetooth Low Energy for Smart Home Energy Management. Energies 8, 11916–11938, 2015.

[14] Collotta M., Pau G., An innovative approach for forecasting of energy requirements to improve a smart home management system based on BLE. IEEE Trans. on Green Commun. and Net. 1.1, 112-120, 2017.

[15] Fan W., Liu N., Zhang J., An Event-Triggered Online Energy Management Algorithm of Smart Home: Lyapunov Optimization Approach. Energies 9, 381, 2016.

[16] Fan W., Liu N., Zhang J., Lei J., Online Air-Conditioning Energy Management under Coalitional Game Framework in Smart Community. Energies 9, 689, 2016.

[17] Nguyen T.T.K., Shimada K., Ochi Y., Matsumoto T., Matsugi H., Awata T., An Experimental Study of the Impact of Dynamic Electricity Pricing on Consumer Behavior: An Analysis for a Remote Island in Japan. Energies 9, 1093, 2016.

[18]Sustainable Energy Authority of Ireland,

www.seai.ie/Your_Business/Large_Energy_Users/Resources/Energy_Management_Systems/

[19] Organizaţia Naţiunilor Unite pentru dezvoltare industrială, Ghid Practic pentru Implementarea unui Sistem de Management Energetic, https://odimm.md/files/ecologizare/Ghid-Practic-implementare-EnMS-rom.pdf

[20] Standard ISO 50001: 2018 Managementul energiei – Sisteme de management al energiei. 

[21] Monitoring and Targeting – in-depth management guide – Carbon Trust,

http://www.carbontrust.com/media/31683/ctg008_monitoring_and_targeting.pdf

[22] Ordinul 1287/2018 pentru aprobarea Ghidului de finanţare a Programului privind instalarea sistemelor de panouri fotovoltaice pentru producerea de energie electrică, în vederea acoperirii necesarului de consum şi livrării surplusului în reţeaua naţională

[23] Dragomir O.E., Dragomir F., Forcasting of renewable energy load with radial basis function (RBF) neural networks. The 8th International Conference on Informatics in Control, Automation and Robotics, (pp. 409-412), 2011. Noordwijkerhout, Olanda.

[24] Dragomir O. E., Dragomir F., MLP neural network as load forecasting tool on short- term horizon. The 19th Mediterranean Conference on Control and Automation, (pp. 1265 – 1270), 2011 Corfu, Grecia.