This paper presents an in-depth description of the onlineforecast
methodology.
Peder Bacher, Henrik Madsen, Henrik Aalborg Nielsen, Bengt Perers
This paper presents a method for forecasting the load for space heating
in a single-family house. The forecasting model is built using data from
sixteen houses located in Sønderborg, Denmark, combined with local
climate measurements and weather forecasts. Every hour the hourly heat
load for each house the following two days is forecasted. The forecast
models are adaptive linear time-series models and the climate inputs
used are: ambient temperature, global radiation and wind speed. A
computationally efficient recursive least squares scheme is used. The
models are optimized to fit the individual characteristics for each
house, such as the level of adaptivity and the thermal dynamical
response of the building, which is modeled with simple transfer
functions. Identification of a model, which is suitable for all the
houses, is carried out. The results show that the one-step ahead errors
are close to white noise and that practically all correlation to the
climate variables are removed. Furthermore, the results show that the
forecasting errors mainly are related to: unpredictable high frequency
variations in the heat load signal (predominant only for some houses),
shifts in resident behavior patterns and uncertainty of the weather
forecasts for longer horizons, especially for solar radiation.
Peder Bacher, Henrik Madsen, Henrik Aalborg Nielsen
This paper describes a new approach to online forecasting of power
production from PV systems. The method is suited to online forecasting
in many applications and in this paper it is used to predict hourly
values of solar power for horizons of up to 36 hours. The data used is
fifteen-minute observations of solar power from 21 PV systems located on
rooftops in a small village in Denmark. The suggested method is a
two-stage method where first a statistical normalization of the solar
power is obtained using a clear sky model. The clear sky model is found
using statistical smoothing techniques. Then forecasts of the normalized
solar power are calculated using adaptive linear time series models.
Both autoregressive (AR) and AR with exogenous input (ARX) models are
evaluated, where the latter takes numerical weather predictions (NWPs)
as input. The results indicate that for forecasts up to two hours ahead
the most important input is the available observations of solar power,
while for longer horizons NWPs are the most important input. A root mean
square error improvement of around 35 % is achieved by the ARX model
compared to a proposed reference model.
Lisa Buth Rasmussen, Peder Bacher, Henrik Madsen, Henrik Aalborg
Nielsen, Christian Heerup, Torben Green
This paper presents a novel study of models for forecasting the
electrical load for supermarket refrigeration. The data used for
building the models consists of load measurements, local climate
measurements and weather forecasts. The load measurements are from a
supermarket located in a village in Denmark. Every hour the hourly
electrical load for refrigeration is forecasted for the following 42 h.
The forecast models are adaptive linear time series models. The model
has two regimes; one for opening hours and one for closing hours, this
is modeled by a regime switching model and two different methods for
predicting the regimes are tested. The dynamic relation between the
weather and the load is modeled by simple transfer functions and the
non-linearities are described using spline functions. The results are
thoroughly evaluated and it is shown that the spline functions are
suitable for handling the non-linear relations and that after applying
an auto-regressive noise model the one-step ahead residuals do not
contain further significant information.