Energy Forecasting
Electricity demand reflects the activity of our day-to-day lives, showing decreased demand on weekdays (top panel in above figure) and during summer vacations (bottom panel). Any mathematical model for electricity demand must be capable of describing a a wide range of effects. These include: (i) long-term changes; (ii) intraday and intraweek seasonality; (iii) yearly seasonality; (iv) special calender events; and (v) weather-induced effects.
Medium to long-term forecasting
Adequate capacity planning requires accurate forecasts of the future magnitude and timing of peak electricity demand. The project has developed a simple nonlinear model that provides probabilistic forecasts of both magnitude and timing over short, medium and long term. Accuracy comparisons have made for many single-signal methods for short-term electricity demand forecasting for lead times up to one day ahead. Having access to probabilistic forecasts provides a means of assessing the uncertainty in the predictions and can lead to improved decision-making and better risk management [1].
Short-term forecasting
A separate study [2] compared 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 interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro, and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives.
[1] P.E. McSharry, S. Bouwman, G.L. Bloemhof (2005), Probabilistic forecasts of the magnitude and timing of peak electricity demand. IEEE Transactions on Power Systems, 20(2): pp. 1166-1172.
[2] J.W. Taylor, L.M. de Menezes, P.E. McSharry (2006), A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting, , (in press).