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...

To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment de...

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 studies the modeling and optimization of energy use and greenhouse gas emissions of eggplant production using artificial neural network and multi-objective genetic algorithm in guilan province of iran. results showed that the highest share of energy consumption belongs to diesel fuel (49.24%); followed by nitrogen (33.30%). the results indicated that a total energy input of 13910.67 ...

Hydrates are crystalline compounds similar to ice, with guest molecules like methane and ethane trapped inside cavities or cages formed by the hydrogen bounded framework of water molecules. These solid compounds give rise to problems in the natural gas oil industry because they can plug pipelines and process equipments. Low dosage hydrate inhibitors are a recently developed hydrate control tech...

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...