Electric Load Consumption Research Articles

A multivariate descriptor method for change-point detection in nonlinear time series

The purpose of this paper is to present a novel method that is applied to detect dynamic changes in nonlinear time series. The method combines a multivariate control chart that monitors the variation of three normalized descriptors – Hjorth's descriptors of activity, mobility and complexity – and is applied to the change-point detection problem of nonlinear time series. The approach is estimated using six simulated nonlinear time series. In addition, a case study of six time series of short-term electricity load consumption was used to illustrate the power of the method.

Mining power and hydrocarbon consciousness from the monthly electricity bill: a classroomproject

Residential monthly electricity bills provided by students in physics classes served as datafor a project designed to help them develop a sense of scale for electric energyconsumption referenced to their own electric lifestyles and insight into how theselifestyles depend heavily on various naturally occurring terrestrial hydrocarbonresources. From kilowatt-hours (kWh) reported on the bill, we determinedper capita averages for electric power load (661 W) and energy consumption(476 kWh/month) which provide a quantitative framework for discussions about personal and societalelectric energy use and illustrate how physics provides the fundamental vocabulary andanalytical perspective for comprehending and managing our various local and global energydilemmas. Interpretation and presentation of these data could be modified for audiencesranging from middle-school physical science classes to university-level environmentalscience classes.

Neural network based short-term load forecasting using weather compensation

This paper presents a novel technique for electric load forecasting based on neural weather compensation. Our proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. Our weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error.

Nonlinear autoregressive integrated neural network model for short-term load forecasting

A novel neural network technique for electric load forecasting based on weather compensation is presented. The proposed method is a nonlinear generalisation of Box and Jenkins approach for nonstationary time-series prediction. A nonlinear autoregressive integrated (NARI) model is identified to be the most appropriate model to include the weather compensation in short-term electric load forecasting. A weather compensation neural network based on an NARI model is implemented for one-day ahead electric load forecasting. This weather compensation neural network can accurately predict the change of electric load consumption of the coming day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing more accurate load forecast with a 0.9% reduction in forecast error.