Review and classification of electric load forecasting (LF) techniques based on artificial neuralnetworks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANNoriented applications for forecasting are given in the literature. These are classified into five groups:(1) ANNs in short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term LF, (4) Hybrid ANNs inLF,...

The paper tests the unbiasedness of interest differentials and term structure as predictors of inflation differentials and inflation changes, respectively, using three-, sixand twelve-month maturities in eight major industrial countries over the period 1981—1992. The first hypothesis requires rational expectations (RE) and equality of ex-ante real interest rates, which in turn holds only in the...

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...

Paper presents a simplistic approach towards the detection and dynamics analysis of volcanic eruptions represented as unevenly spaced spatio-temporal time series of satellite retrieved hot spots. The paper discusses isolation and interpolation of hot spots data produced by Nightfire algorithm for the purposes of short-term volcanic activity ARIMA based forecasting. The case study for Chirpoi Sn...

The predictive value of long-termenergy scenario models is close to none.Why then do we use these models, notably for ongoing activities of theIPCC? This essay briefly describes tworecent examples demonstrating thatparticularly integrated assessmentmodels that simulate or optimizeenergy-economy-climate interactionspossess no practical use when it comesto fore...

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

In this paper we investigate the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the with-insample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for comparative purposes. Two forecasting methods, 1step-ahead and multi-step-ahead forecasting are compared for eac...

Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...