In smart grid, precise and accurate electricity load forecasting is one of the most challenging
tasks. It is due to the high volatile, non-stationary and non-linear behavior of electricity load …

Forecasting the daily load demand of an electric utility provider is a complex problem as it is
nonlinear and influenced by external factors. Deep learning, machine learning and artificial …

The availability of electricity demand is very high. Many households and industrial
equipment are using electricity as the source energy. The reliability of the power system in …

Electricity load forecasting plays an important role in the energy planning such as
generation and distribution. However, the nonlinearity and dynamic uncertainties in the …

The increasing dependency on electricity and demand for renewable energy sources means
that distributed system operators face new challenges in their grid. Accurate forecasts of …

It is critical to maintain a balance between the supply and the demand for electricity because
of its non-storable feature. For power-producing facilities and traders, an electrical load is a …

Accurate electric load forecasting is important due to its application in the decision making
and operation of the power grid. However, the electric load profile is a complex signal due to …

Deep learning has been proven of great potential in various time-series forecasting
applications. To exploit the potential and extendibility of deep learning in electricity load …

The objective of this research is to improve the short-term load forecasting accuracy using
deep learning models such as long short-term memory (LSTM) and deep belief network …

One of the most important research topics in smart grid technology is load forecasting,
because accuracy of load forecasting highly influences reliability of the smart grid systems …