Energy Management System for the Campus Microgrid Using an Internet of Things as a Service (IoTaaS) with Day-ahead Forecasting

Author:

Zhakiyev Nurkhat,Satan Aidos,Akhmetkanova Gulnar,Medeshova Aigul,Omirgaliyev Ruslan,Bracco Stefano

Publisher

Elsevier BV

Reference14 articles.

1. International Energy Agency. 2021. "World Energy Outlook 2021." Accessed December 13, 2023. https://www.iea.org/reports/world-energy-outlook-2021.

2. European Commission. 2021. "EU Climate Action Progress Report 2021." Accessed December 13, 2023. https://ec.europa.eu/clima/sites/clima/files/docs/pages/com_2021_564_en.pdf.

3. Comparative LSTM and SVM Machine Learning Approaches for Energy Consumption Prediction: Case Study in Akmola;Satan,2022

4. Zhakiyev, N., Amanbek, Y., Kalakova, A., Yedilkhan, D. (2023). Home Energy Management System to Reduce Unconscious Electricity Consumption. In: Rabie, K., Kharel, R., Mohjazi, L., Elganimi, T., Khan, W.U. (eds) IoT as a Service. IoTaaS 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-031-37139-4_1.

5. Smart grid technologies: Communication technologies and standards;Gungor;IEEE transactions on Industrial informatics,2011

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