Comparative Study of Load Forecasting Techniques in Smart Microgrid
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-0915-5_18
Reference14 articles.
1. Elsaraiti M, Ali G, Musbah H, Merabet A, Little T (2021) Time series analysis of electricity consumption forecasting using ARIMA model. In: 2021 IEEE Green technologies conference (GreenTech), pp 259–262, June 2021
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3. Thejus S, SP (2021) Deep learning-based power consumption and generation forecasting for demand side management. In: 2021 Second international conference on electronics and sustainable communication systems (ICESC), pp 1350–1357, September 2021
4. Yahya MA, Hadi SP, Putranto LM (2018) Short-term electric load forecasting using recurrent neural network (study case of load forecasting in central java and special region of yogyakarta). In: 2018 4th International conference on science and technology (ICST), pp 1–6
5. Sun D, Qinghai O, Yao X, Gao S, Wang Z, Ma W, Li W (July2020) Integrated human-machine intelligence for EV charging prediction in 5G smart grid. EURASIP J Wirel Commun Netw 2020(1):1–15
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