Short-term load forecasting (STLF) is an important but a difficult task due to the uncertainty and complexity of electric power systems. In recent times, attention-based model, Informer, has been proposed for efficient feature learning lone sequences. To solve quadratic traditional method, this model designs what called ProbSparse self-attention mechanism. However, mechanism may neglect daily-c...

The realization of load forecasting studies within the scope periods varies depending on application areas and estimation purposes. It is mainly carried out at three intervals: short-term, medium-term, long-term. Short-term (STLF) incorporates hour-ahead forecasting, which critical for dynamic data-driven smart power system applications. Nevertheless, based our knowledge, there are not enough a...

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are dec...

The worldwide electric power industry has seen many changes over the last 20 years. During this period many regulated or state-owned monopoly markets have been deregulated. In an electricity market, electricity price is decided based on demand and supply bids from the market participants; therefore, the importance of ShortTerm Load Forecasting (STLF) has been rising in these markets [1]. Load f...

Short-term load forecasting (STLF), especially for regional aggregate forecasting, is essential in smart grid operation and control. However, the existing CNN-based methods cannot efficiently extract features from electricity load. The reason that basic requirement of using CNNs space invariance, which not satisfied by actual data. In addition, models multi-scale input representing tendency loa...

Procurement of electricity gains more and more focus in enterprises especially with the introduction of electric mobility. At a maritime container terminal the electricity consumption is highly related to the number of container movements of each day. Short-term load forecasting (STLF) methods have not yet been systematically researched when applied to container terminals. Therefore it seems re...

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are dec...

Short-term load forecasting (STLF) is an obligatory and vibrant part of power system planning dispatching. It utilized for short running targets in planning. Electricity consumption has nonlinear patterns due to its reliance on factors like time, weather, geography, culture, some random individual events. This research work emphasizes STLF through profile data from domestic energy meter forecas...

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are dec...

Electricity demand forecasting remains a challenging issue for power system scheduling at varying stages of energy sectors. Short Term load (STLF) plays vital part in regulated systems and electricity markets, which is commonly employed to predict the outcomes failures. This paper presents an intelligent machine learning with evolutionary algorithm based STLF model, called (IMLEA-STLF) involves...