Plug-in hybrid electric vehicles (PHEVs) use grid electricity as well as on-board gasoline for motive force. These multiple energy sources make prediction of PHEV energy consumption challenging and also complicate evaluation of their environmental impacts. This paper introduces a novel PHEV energy consumption modeling approach and compares it to a second approach from the literature, each using...

Evidence suggests that rational alternatives do not necessarily drive the choices made by real individuals in the privacy of their homes, Therefore, an understanding of the characteristics of the utility’s customer base, is useful to help design utility programs that target the customer’s ordinary daily practices. This paper summarizes the main results of a method to highlight familial habits b...

A major cause for many of the problems that have afflicted wholesale electricity markets is the unrealized potential for the demand-side to be a full participant. Ironically, when Fred Schweppe first envisioned an electricity market in which prices efficiently coordinated the actions of market participants, he focused on the potential for improved efficiency in power consumption, not generation...

Utilities use Demand Response (DR) to balance supply and demand in the electric grid by involving customers in efforts to reduce electricity consumption during peak periods. To implement and adapt DR under dynamically changing conditions of the grid, reliable prediction of reduced consumption is critical. However, despite the wealth of research on electricity consumption prediction and DR being...

A system for visualizing electricity consumption data is presented in this paper, along with the implementation of a pattern recognition approach for peak prediction. Various classi cation algorithms and machine learning techniques are tested and discussed; in particular, Support Vector Machine (SVM), Gaussian Mixture Model (GMM) and hierarchical classi ers. Most notably, the best algorithms ar...

" …the price of electricity was found to be a significant determinant of electricity demand in most cases with the expected inverse relationship (higher prices mean less consumption).The results provided no evidence that the responsiveness of Ontario industrial demand to the price of electricity during time periods when demand is the strongest has strengthened since market opening except, perha...

Consumption of energy is influenced by a multitude of factors. Beyond factors determining the demand for a specific energy service, characteristics of and attitudes to the energy product delivering the service can also strongly affect this demand. Attributes of electricity which may influence the demand for electricity services include its cost, its environmental impacts, and the social/politic...

The most popular way to improve consumers’ control over their electricity cost is by providing frequent and detailed feedback with “in-home displays” (IHD). In this study, we examined alternative ways to train experimental participants to control and optimize their use of electricity by “feedforward” training to map energy consuming behaviors to costs. The participants were trained in one of fo...

The security of power consumption demand is an essential issue in maintaining a rapid economic development in China. Owing to the advantages such as the robustness and favorable estimation performances, the semiparametric model and nonparametric models have obtained wide popularity. In this article, based on the semiparametric and nonparametric models, a new estimation model is created, and the...

Electricity demand and supply forecasts assume special significance vis-Ii-vis ever-increasing demand. Different agencies and authors, to forecast demand, have used various methodologies but on the other hand, no concerted effort is visible in predicting the supply. Moreover none of the methodologies used can be considered perfect. The present study attempts to forecast the two and tends to rea...