The forecast of electricity demand has been a recurrent research topic for decades, due to its
economical and strategic relevance. Several Machine Learning (ML) techniques have …

In this paper, a systematic literature review is presented, through a survey of the main digital
databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly …

The electricity consumption by industrial customers in the society accounts for a significant
proportion of the total electrical energy. Thus, it is of great significance for demand-side …

This paper addresses the mid-term electricity load forecasting problem. Solving this problem
is necessary for power system operation and planning as well as for negotiating forward …

Short term electric load forecasting plays a crucial role for utility companies, as it allows for
the efficient operation and management of power grid networks, optimal balancing between …

The rising popularity of deep learning can largely be attributed to the big data phenomenon,
the surge in the development of new and novel deep neural network architectures, and the …

This work presents a hybrid and hierarchical deep learning model for midterm load
forecasting. The model combines exponential smoothing (ETS), advanced long short-term …

The electric load forecasting is extremely important for energy demand management,
stability and security of power systems. A sufficiently accurate, robust and fast short-term …

Load forecasting is useful for various applications, including maintenance planning. The
study of load forecasting using recent state-of-the-art hybrid artificial intelligence (AI) and …

Long-term load forecasting (LTLF) is an essential process for strategical planning of the
future needed extension in the power systems of any country. Besides, deep learning has …