We propose a simple empirical scaling law that describes load forecasting accuracy at different levels of aggregation. The model is justified based on a simple decomposition of individual consumption patterns. We show that for different forecasting methods, aggregating more customers improves the relative forecasting performance up to specific point. Beyond this point, no more improvement in re...

EAF plants normally consume coal and coke as reactants, alloying elements, or fuel. The price of these materials varies from € 0.20-0.35/kg. This is while nearly 10 million pieces of tires are annually discarded in Iran. The present study shows that scrap tires can be used as a substitute for coal and coke in EAF plants. Thirteen grades of steel including low, medium, and high carbon steels as ...

Due to various seasonal and monthly changes in electricity consumption, it is difficult to model it with conventional methods. This paper illustrates an Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network for household electricity consumption forecasting. This is the first study which uses MLP for forecasting household electricity consumption. Previ...

This paper evaluates the treatment of textile dyeing wastewater by using steel scrap as adsorbents and heterogeneous catalysts for Modified Fenton oxidation. The efficiency of the process was explored as a function of the experimental parameters: pH, hydrogen peroxide concentration and Steel scrap content. The composites with high steel scrap content were effective to adsorb colour and COD in t...

Water is most essential for existence of life on earth. Water is the basic necessity of all living creatures and its important use are for drinking purpose. Water intended for human consumption must be safe and free from microbes. Therefore this investigation was taken with the objective to study the effect of storage of lake water in different vessels like plastic, clay, copper and stainless s...

Lettau and Ludvigson (2001) find that the consumption-wealth ratio (cay) constructed from revised data is a strong predictor of stock market returns. This paper shows that its out-ofsample forecasting power becomes substantially weaker if cay is estimated using information available at the time of forecast. The difference, which mainly reflects periodic revisions in consumption and labor income...

Monthly forecasting of electric energy consumption is important for planning the generation and distribution of power utilities. However, the features of this time series are so complex that directly modeling is difficult. Three kinds of relatively simple series can be derived when a discrete wavelet transform is used to extract the raw features, namely, the rising trend, periodic waves, and st...

Load forecasting allows electric utilities to enhance energy purchasing and generation, load switching, contracts negotiation and infrastructure development [1]. The consumption regions have characteristic consumption profiles which determine a causal relationship between the load and a set of predictors. For short term load forecasting, in which the predictions range from few minutes to some d...

Due to imprecision and uncertainties in predicting real world problems, artificial neural network (ANN) techniques have become increasingly useful for modeling and optimization. This paper presents an artificial neural network approach for forecasting electric energy consumption. For effective planning and operation of power systems, optimal forecasting tools are needed for energy operators to ...

This paper proposes simple and computationally efficient forecasting algorithms for a KuhnTucker (KT) consumer demand model system called the Multiple Discrete-Continuous Extreme Value (MDCEV) model. The algorithms build on simple, yet insightful, analytical explorations with the Kuhn-Tucker conditions of optimality that shed new light on the properties of the model. Although developed for the ...