There are a lot of uncertainties in planning and operation of electric power system, which is a complex, nonlinear, and non-stationary system. Advanced computational methods are required for planning and optimization, fast control, processing of field data, and coordination across the power system for it to achieve the goal to operate as an intelligent smart power grid and maintain its operatio...

The prediction accuracy of short-term load forecast (STLF) depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF)-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original fe...

A keypoint of the control of a power system is the forecast of the short term load. This paper presents a dynamic model for short-term load forecasting (STLF) which uses a recurrent neural network. This network can be used to build empirical models for the load of a dynamic system. We investigate this problem applying a basic neural network with feedback connections which is unfolded in time an...

This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets | one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was ...

In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and ...

A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and Internet things (IoT), where digitalization at core energy sector transformation. However, grids require managers become more concerned about reliability security systems. Therefore, planners use various methods technologies to support sustainable expansion systems, such as ele...

The important, while mostly underestimated, step in the process of short-term load forecasting–STLF is selection similar days. Similar days are identified based on numerous factors, such as weather, time, electricity prices, geographical conditions and consumers’ types. However, those factors influence differently within different circumstances conditions. To investigate optimise process, a new...

This paper proposes a novel neural model to the problem of short-term load forecasting (STLF). The neural model is made up of two self-organizing map (SOM) nets—one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained on load data extracted from a Brazilian electric utility, and compa...

An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces overall uncertainty added by intermittent production renewable sources; thus, it helps to minimize hydrothermal electricity costs in a grid. Although there some research field and even several applications, continual need improve forecasts. This proposes set...

The grey model GM(1,1) based on the grey system theory has recently emerged as a powerful tool for short term load forecasting (STLF) problem. Since GM(1,1) is only first order dynamic grey model, the accuracy is not satisfactory when original data show great randomness. In this paper, we proposed improved dynamic mode GM(2,1) to enhance forecasted accuracy. Then it is applied to improve STLF p...