Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly and it also reduces the generation cost and increases reliability of power systems. In this work, a fuzzy logic approach for short term load fore...

With the development of economy and the progress of science, the proportion of electrified railway load in the power gird has been keeping on increasing, which impacts the short-term forecasting in load a lot, therefore, it is very important to analyze short-term load of electrified railway forecasting. This paper analyzes the power gird load-forecasting considering the influence of the electri...

Prediction of peak loads in Iran up to year 2011 is discussed using the Radial Basis Function Networks (RBFNs). In this study, total system load forecast reflecting the current and future trends is carried out for global grid of Iran. Predictions were done for target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load forecasting is mainly affected by economy...

using electric vehicles, in addition to decreasing the environmental concerns, can play an important role in decreasing the peak and filling the off-peaks of the daily load characteristics. in other words, in smart grids' infrastructure, the load characteristics can be improved by scheduling the charge and discharge process of electric vehicles. in smart grids, the customers are instantane...

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 ...

The problem of short-term electric load forecasting (STLF) is considered. A modified architecture of Elman-type recurrent neural network is proposed. It utilizes a special fuzzification layer to deal with quantitative as well as ordinal and nominal data. The second hidden layer of the network consists of standard Rosenblatt-type neurons with sigmoidal activation functions. The context layer is ...

Short Term Load Forecasting (STLF) is a power system operating procedures that have an important role in terms of realizing the economic electric production. This research focuses on the application of hybrid PSO-ANN algorithm in STLF. Load data grouped by the type of weekdays and holidays. Consumption of electricity load in West Java Indonesia, used as input to the learning algorithm PSO-ANN. ...

The electric power load forecasting is critical for stable electric power system supply. In this paper, a seasonal ARIMA model was used to effectively forecast power load data characterized using periodicity. A numerical example reveals that the seasonal ARIMA model effectively forecast periodic power load.

Load forecasting is a central integral process in the planning and operation of electric utilities. Load forecasting has become in recent years one of the major areas of research in electrical engineering. The main problem for the planning is the determination of load demand in the future. Because electrical energy cannot be stored appropriately, correct load forecasting is very essential for t...