In this paper, we presented the performance of forecasting model and error correction will affect the accuracy of short-term load forecasting. Least squares support vector machines (LS-SVM) based on improved particle swarm optimization is selected as load forecasting model. Forecasting accuracy and generalization performance of LS-SVM depend on selection of its parameters greatly. Adaptive part...

Problem statement: Load forecasting plays an important task in power system planning, operation and control. It has received an increasing attention over the years by academic researchers and practitioners. Control, security assessment, optimum planning of power production required a precise short term load forecasting. Approach: This study tries to combine neural network and fuzzy logic for ne...

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 aims models electricity load curves for short-term forecasting purposes. A broad class of multivariate dynamic regression model is proposed to model hourly electricity load. Alternative forecasting models, special cases of our general model, include separate time series regressions for each hour and week day. All the models developed include components that represent trends, seasons ...

1 Abstract In this paper we present an application of predictive modular neural networks (PREMONN) to short term load forecasting. PREMONNs are a family of probabilistically motivated algorithms which can be used for time series prediction, classification and identification. PREMONNs utilize local predictors of several types (e.g. linear predictors or artificial neural networks) and produce a f...

This paper presents the implementation of an adaptive load forecasting methodology in two different power networks from a smart grid demonstration project deployed in the region of Madrid, Spain. The paper contains an exhaustive comparative study of different short-term load forecast methodologies, addressing the methods and variables that are more relevant to be applied for the smart grid depl...