Deep belief network based electricity load forecasting: An analysis of Macedonian case

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Forecasting Crude Oil Prices: a Deep Learning based Model (2017, methodology): “[11] used the deep belief network model to predict the short-term power load in Macedonia from 2008 to 2014.”
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, methodology): “[232] use a Deep Belief Network for electricity load forecasting in the city of Macedonia.”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, methodology): “[122] employed the average load of the previous day, the load for the same hour of the previous day and the load for the same hour-day combination of previous week as historical data to forecast day ahead hourly electricity load for buildings in Macedonia.”
Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting (2016, unknown): “[41].”
A Review on Digital Twin Technology in Smart Grid, Transportation System and Smart City: Challenges and Future (2023, background): “Deep belief network (DBN) compound with parametric Copula models for forecasting hourly load is suggested in [20].”
Deep neural networks for energy load forecasting (2017, methodology): “In addition to these methods, authors in [22] and in [23] presented Deep Belief Networks for load forecasting.”