P. E. McSharry

Publication List Details

Period

2002 - 2009

Number

13

Co-Authors

1 Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models (2009)

J. W. Taylor, P. E. Mcsharry, Senior Member, R. Buizza

Abstract-- Wind power is an increasingly used form of renewable energy. The uncertainty in wind generation is very largely due to the inherent variability in wind speed, and this needs to be...

THE EFFECT OF LOCAL WEATHER CONDITIONS ON THE LUNG FUNCTION OF MILD-TO-MODERATE ASTHMATICS (2008)

W. R. Cobern, P. E. Mcsharry, L. Tarassenko

Abstract – The correlation between severe asthma attacks and weather conditions such as thunderstorms has been known for some time. However, the effect of local weather on the measured values of...

CLINICAL EVALUATION OF A MOBILE PHONE TELEMEDICINE SYSTEM FOR THE SELF-MANAGEMENT OF TYPE 1 DIABETES (2008)

O. J. Gibson, L. Tarassenko, P. E. Mcsharry, P. M. Hayton, A. J. Farmer

Abstract – A mobile phone system for the selfmanagement of Type 1 diabetes was tested in a clinical randomized controlled trial. Both intervention and control groups used the system, but the...

Bayesian objective classification of extreme UK daily rainfall for flood risk applications (2008)

M. A. Little, P. E. McSharry

In this study we describe an objective classification scheme for extreme UK daily precipitation to be used in flood risk analysis applications. We create a simplified representation of the spatial...

A comparison of univariate methods for forecasting electricity demand up to a day ahead (2006)

Taylor, J.W., De Menezes, L.M., McSharry, P.E.

This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular...

A comparison of univariate methods for forecasting electricity demand up to a day ahead (2006)

Taylor, J. W., De Menezes, L. M., McSharry, P. E.

This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular...