LOADFOR (Load Forecasting) predicts the load in electrical power systems and delivers both highly competitive and accurate predictions. The maximal prediction horizon is determined by the meteorological forecast used and is typically one week.
LOADFOR is based on models developed for scenario analysis and long term forecasting, developed at the Technical University of Denmark over the period 1994-2000. Originally, the models were developed based on 20 years of hourly observations of power load and climate for a larger geographical region. Following this development the models were adapted for analytical purposes in cooperation with an electric utility. Finally the models were further developed and optimized for online operation, where the continuous flow of on-line observations are utilized.
Input to LOADFOR
LOADFOR takes the following data is taking into account:
Online measurements of power load.
Met. forecasts of the local air temperature, wind speed, and solar radiation for horizons up to the desired operational horizon, typically one week.
If available, climate measurements can be included also. Furthermore, by configuration, calendar information corresponding to the particular geographical region is used by LOADFOR.
Output from LOADFOR
LOADFOR produce online predictions of the power load for horizons up to the horizon covered by the meteorological forecasts, whereas the resolution in time can be less than that of the meteorological forecast.
As for other ENFOR forecast products the forecasts are supplied together with standard uncertainty intervals. For longer horizons where the meteorological forecast uncertainty may be significant the standard forecasts can be supplemented with quantile forecasts, which are highly reliable representations of the uncertainty.
In order to include the development over time in the representation of uncertainty reliable load scenarios can further supplement the standard forecasts. These scenarios can furthermore be generated jointly with other ENFOR scenarios as e.g. heat load or wind power production scenarios. In this way all relevant auto- and cross-correlations can be accounted for.
LOADFOR as a web-service
If desired, LOADFOR can be installed as a service on servers managed by us.
Methods underlying LOADFOR
LOADFOR is built on artificial intelligence, and hence the system automatically calibrates to the actual situation. For instance the models automatically take into account changes in the user profiles, the the consumer behavior around holiday periods, changes in the use of cooling, changes in the MET forecasts, etc.
The models are typically a mixture of parametric, semi-parametric and non-parametric models. As an example the effect of holiday periods are most often described by using splines, which allow for a high level of flexibility around the start and the end of a holiday period. Another example is the effect of the solar radiation (direct and diffuse) which most often is appropriately described by a non-parametric function, since the turn off of light as a function of the radiative components can not adequately be described by a parametric function. Some of the functions in LOADFOR are static functions, whereas as other functions are dynamic functions. As an example the influence of the outdoor air temperature is most often dynamically due to heat accumulation in buildings - provided that a part of the power is used for heating or cooling.
Below some further details regarding LOADFOR are presented.