| dc.description.abstract | This master thesis is analyzing short-term load forecasting. Power consumption in kW will be
forecasted 24 hours ahead, for each day of a week and finally averaged to derive mean
performance. The forecast will be conducted by selected methods and models and compared
against a simple yet reasonable benchmark model. To evaluate the performance in detail, we
select to compute MAPE values for each individual hour, day and average over one week. In
addition, we construct a tailored evaluation metric to estimate the economic consequences of
inaccurate load forecasts. This master thesis is intended to provide a theoretical and empirical
link between contemporary forecasting techniques and actual economic benefits that can be
derived from improved accuracy of load forecasts at Skagerak Energilab.
Obtained results show a tendency of increased forecasting accuracy when utilizing machine
learning algorithms with Neural Network structures. However, no single method could
outperform an ensemble average model. Compared to the benchmark model, our proposed
Ensemble consisting of BATS, seasonal ARIMA, and a multivariate AR ANN increased
forecasting accuracy by a notable degree. Also, improved performance was shown to result in
a decreased direct economic cost. | en_US |
