Short-term load forecasting (STLF) plays an important role in the operational planning security functions of an energy management system. The short term load forecasting is aimed at predicting electric loads for a period of minutes, hours, days or week for the purpose of providing fundamental load profiles to the system. The work presented in this paper makes use of PSO based local linear wavel...

In power systems the next day’s power generation must be scheduled every day, day ahead short-term load forecasting (STLF) is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and in turn, increases the operating cost by using expensive peaking ...

Short term load forecasting can be made effective and closer to actual demand by applying the suggested multi pronged approach of genetic, fuzzy and statistical method as discussed in this paper. Taking the advantages of global search abilities of evolutionary computing as well as expert inference based on statistical aspects, load forecasting can be made nearly error free. The results were com...

In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make ...

This paper discusses significant role of advanced technique in short-term load forecasting (STLF), that is, the forecast of the power system load over a period ranging from one hour to one week. An adaptive neuro wavelet time series forecast model is adopted to perform STLF. The model is composed of several neural networks (NN) whose data are processed using a wavelet technique. The data to be ...

Short-term electric load forecasting (STLF) plays the main role in making operational decisions in any electrical power system. The implementation of forecasting algorithms collides with the high computational power needed to perform the complex perdition processes on large datasets. In this paper, a cloudbased STLF algorithm is implemented. The performance analysis of the proposed system was c...

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to reso...

In the paper the problem of the application of neural predictors for Short-Term Load Forecasting (STLF) for energy transactions planning in utility is presented. Several aspects of this topic are discussed, including identification of different load patterns for holidays and customer profiles, estimation of prediction intervals and optimal size of the order, according to the financial condition...

Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural netwo...

Short Term Load Forecasting(STLF) varies from an hour to hour and is used for requirement for control, unit commitment, security assessment, optimum planning of power generation, and planning of both spinning reserve and energy exchange, also as inputs to load flow studies and contingency analysis. Artificial neural networks (ANN’s) has drawbacks like inputs nodes or hidden nodes which can caus...