This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple...

Load forecasting is essential for planning and operation in energy management. It enhances the Energy efficient and reliable operation of a power system. The energy supplied by utilities meets the load plus the energy lost in the system is ensured by this tool. Since in power system the next day’s power generation must be scheduled every day. The dayahead short term load forecasting (STLF) is a...

This paper presents a new approach for short-term load forecasting (STLF). Curve fitting prediction and time series models are used for hourly loads forecasting of the week days. The curve fitting prediction (CFP) technique combined with genetic algorithms (GAs) is used for obtaining the optimum parameters of Gaussian model to obtain a minimum error between actual and forecasted load. A new tec...

Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used...

Electric load forecasting is a fundamental business process and well-established analytical problem in the utility industry. Due to various characteristics of electricity demand series and the business needs, electric load forecasting is a classical textbook example and popular application field in the forecasting community. During the past 30 plus years, many statistical and artificial intelli...

Load forecasting is an important problem in the operation and planning of electrical power generation. To minimize the operating cost, electric supplier will use forecasted load to control the number of running generator unit. Short-term load forecasting (STLF) is for hour to hour forecasting and important to daily maintaining of power plant. Most important factors in load forecasting includes ...

Kernel-based methods, such as support vector regression (SVR), have demonstrated satisfactory performance in short-term load forecasting (STLF) application. However, the good performance of kernel-based method depends on the selection of an appropriate kernel function that fits the learning target, unsuitable kernel function or hyper-parameters setting may lead to significantly poor performance...

Load forecasting allows for the utilities to plan their operations to serve their customers with more reliable and economical electric power. With the developments in computer and information technology new techniques to accurately forecast power system loading are emerging. This research culminates in development of modified algorithms for short-term load forecasting (STLF) of a utility grade ...

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 ...

This paper presents the application of interval type-2 fuzzy inference systems (IT2FIS) in short term load forecasting (STLF) on special days. This is a continuation work of application interval type-2 fuzzy systems (IT2FSs) using Karnik Mendel algorithm. Special days here mean local Balinese holidays such as national and local culture-based public holidays, consecutive holidays, and days prece...