In the competitive framework of the electricity markets, demand forecasting with high accuracy is invaluable for governmental institutions and power generation industry. By providing a basis for sound decision-making in energy management, it supports the decision makers to build capacity to meet the demand. In this research, we use electricity consumption data from 81 cities in Turkey to create global probabilistic forecasting models. For this purpose, we implement two prevailing probabilistic forecasting methods, namely, DeepAR and Deep State Space (DeepState) models. We compare the performances of these models against well-known statistical forecasting models (e.g., ARIMA), machine learning models (e.g., gradient boosting, and random forests), and deep learning models (e.g., long short term memory networks (LSTM) and gated recurrent unit networks (GRU)). Our results show that LSTM and GRU outperform others in terms of point forecasting performance, whereas DeepAR and DeepState models provide a better probabilistic forecasting performance for longer prediction horizons. We find that the global forecasting methods successfully leverage multiple time series (as in the case of 81-city forecasting data), however, mainly due to the relatively simple structure of the electricity datasets, they do not lead to meaningful performance gain over simpler models.