This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning of demand-side Bulk Electricity Market Customers (BEMCs). The Ontario electricity market is selected as the primary case market and its structure is studied in detail. A set of explanatory variable cand...

Climate change could significantly affect consumer demand for energy in buildings, as changing temperatures may alter heating and cooling loads. Warming climates could also lead to the increased adoption and use of cooling technologies in buildings. We assess residential electricity and natural gas demand in Los Angeles, California under multiple climate change projections and investigate the p...

Planning for electricity demand is a key factor for the success in the development of any countries. Such success can only be achieved if the demand for electricity is predicted correctly and accurately. This study introduces a new hybrid approach that combines Principle Component Regression (PCR) and Back-Propagation Neural Networks (BPNN) techniques in order to improve the accuracy of the ele...

Planning for electricity demand is a key factor for the success in the development of any countries. Such success can only be achieved if the demand for electricity is predicted correctly and accurately. This study introduces a new hybrid approach that combines Principle Component Regression (PCR) and Back-Propagation Neural Networks (BPNN) techniques in order to improve the accuracy of the ele...

Increased demand for cooling and decreased demand for heating in the built environment is one of the main anticipated modes of adaptation to higher temperatures due to climate change. California’s residential sector uses relatively little electricity for heating. It is therefore expected that the demand for electricity will increase at the intensive margin as households operate existing air con...

The major task of a grid operator is perfectly balancing the demand of all customers at any instant with supply. One of the facets of the Smart Grid vision is tackling the problem of balancing demand with supply using strategies that act on the demand side. With the deployment of intelligent ICT devices in domestic environments, homes are becoming smarter and able to optimise the electricity co...

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and leptokurtic caused by the nature of energy data. In this paper we propose a novel approach to estimate the time-varying conditional variance of th...

There has been intense recent interest in the vulnerability of the US electric power sector to the potentially adverse impacts of global warming. In this paper I use a three-phased approach to investigate the consequences of climate-driven temperature increases on electricity supply and demand within US states. Reduced-form long-run responses of electricity demand to temperature are estimated b...

The aim of this paper is to present impact studies of economic and environment related constraints on the German electricity spot market. The analysis was carried out using the system dynamics based model for the German electricity market, called “Zertsim”. The model was applied for the first time in a workshop with entrepreneurs from the region of Karlsruhe in Germany. One result of the worksh...