Throughout this thesis we have investigated how to construct forecasting models for the short-term hourly electric load in Norway, and presented our predictions for the aggregated, and individual price areas. In our findings, a linear Dynamic Harmonic Regression (DHR) model performs better than the non-linear Neural Net-work Autoregression (NNAR) with an average MAPE across the predicted zones of 2,73% and 3,38% for the models respectively. A Multi-Layer Perceptron (MLP) neural network was also created but fails to deliver an adequate forecast accu-racy with a MAPE of 11,98%, performing worse than the baseline seasonal naïve, with 6,40% MAPE.
Exploring the different methods available, a question that often arises is whether artificial intelligence will revolutionize the forecasting field in place of the more traditional statistics. From our results, this is not the case as the DHR provides a lower MAPE of 0,65% compared to the NNAR but might hypothetically have a similar potential for short-term load forecasting depending on the model struction and optimization. For the model performance, the DHR forecasts con-sistently better than the other models across all the zones, weekdays, months, and hours, with just a few exceptions for NO. This might indicate that the Norwegian market has mostly linear relationships with underlying determinants, and that the DHR captures these patterns better than the more complicated MLP, and the hy-brid NNAR.
As for potential model improvements, collecting the weather temperature for the price areas could have been more thorough, for instance constructing an aver-age for the zone depending on population size and density to better capture the effect. Similarly, this could also have been done for precipitation and wind, which was found to reduce model fit in our case. More calendar effects could also have been controlled for, perhaps by examining the model errors to identify further ar-eas of improvement.
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