The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …

Long-term power-system planning and operation, build on expectations concerning future
electricity demand and future transmission/generation capacities. This paper reviews current …

Electricity consumption forecasting is essential for intelligent power systems. In fact, accurate
forecasting of monthly consumption to predict medium-and long-term demand substantially …

Pakistan is in a terrifying and devastating energy crisis. Recently, the prediction for energy
consumption has intensified compared to its production capacity, which is problematic for …

Collections of time series formed via aggregation are prevalent in many fields. These are
commonly referred to as hierarchical time series and may be constructed cross-sectionally …

Forecasting the industry's electricity consumption is essential for energy planning in a given
country or region. Thus, this study aims to apply time-series forecasting models (statistical …

The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

Accurate electricity consumption forecasting plays a crucial role in electric power systems
and is a challenging task due to its complicated mechanism induced by multiple influential …

Data-driven methods, such as artificial neural networks (ANNs), support vector regression
(SVM), Gaussian process regression (GPR), multiple linear regression (MLR), decision trees …

High spatio-temporal resolution estimates of electricity consumption are essential for
formulating effective energy transition strategies. However, the data availability is limited by …