We propose a new approach to forecasting the demand for a commodity in which the commodity supplier asks each consumer to forecast its own demand for the commodity and in return gives a monetary reward that is proportional to the accuracy of the forecast. Such an approach might be applicable when consumer demand for a perishable commodity is uncertain and forecast error leads to waste for produ...

Load forecasting constitutes an important tool for efficient planning and operation of power systems and its significance has been intensifying particularly, because of the recent movement towards open energy markets and the need to assure high standards on reliability. Accurate load forecasting is of great importance for power system operation. It is the basis of economic dispatch, hydrotherma...

In the paper the problem of estimation of the prediction intervals (error bars) for the family neuro-fuzzy Short-Term Load Forecasting (STLF) models is discussed. We investigate two neuro-fuzzy networks: Fuzzy Basis Function (FBF) Networks, and linear neuro-fuzzy model with Tagagi-Sugeno reasoning. The paper contains comparison of selected most important methods for error bars calculation (anal...

In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for po...

Abstract The power systems are important by using short term load forecasting (STLF) because it predicts the in 24 hours ahead or a week ahead. artificial neural network (ANN) brings good result predicted of its accurateness, easiness processing data, construction model as well excellent performances. optimization value ANN is found different methods which consist some weights. This manuscript ...

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...

Different aggregation levels of the electric grid’s big data can be helpful to develop highly accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Whilst different are proposed STLF, they based on small historical datasets and not scalable process large amounts as energy consumption grow exponentially distribution This paper proposes a novel hybrid cluste...

Accurate load forecasting helps stabilize the system by triggering the appropriate actions if needed such as planning for emergency dispatch and load switching for short-term solution and building or upgrading facilities for long-term solution. The Short Term Load Forecasting (STLF) provides information for utilities’ system planners so that they can come up with a short-term solution to protec...

Short-term load forecasting (STLF) is an essential procedure for effective and efficient realtime operations planning and control of generation within a power system. It provides the basis for unit-commitment and power system planning procedures, maintenance schedul‐ ing, system security assessment, and trading schedules. It establishes the generation, capaci‐ ty, and spinning reserve schedules...

Background: Short-term load forecasting is an important issue that has been widely explored and examined with respect to the operation of power systems and commercial transactions in electricity markets. Of the existing forecasting models, support vector regression (SVR) has attracted much attention. While model selection, including feature selection and parameter optimization, plays an importa...