Fredrik Wallin

Electrifying ports transfers energy use from fossil fuel to electricity, but annual energy projections alone do not establish the grid connection and substation capacities required during coincident vessel service events. This study develops a capacity planning framework that converts pathways for throughput, vessel mix, equipment electrification, onshore power supply (OPS), terminal migration and flexibility into ensembles of annual hourly demand trajectories. A multiscale graph state-space model with conformal prediction (MGSS-CP) is used as the mapping from operational events to load rather than recursively forecasting one hour ahead for ten years. Annual maxima provide capacity quantiles, capacity gaps and reinforcement deferral. On the chronological 2025 hold-out, MGSS-CP attains a one-hour mean absolute error of 1.06 MW and an R2 of 0.957, while its 90% interval coverage remains 0.899 to 0.901 across the evaluated horizons. Under the fully electrified pathway, annual demand reaches 625.7 GWh and the central scenario peak reaches 122.6 MW by 2035. Scaling annual energy at the 2025 load factor gives 102.0 MW. It therefore understates the peak by 20.6 MW and the additional capacity above 2025 by about 40%. Alternative event placement raises the peak to about 129 MW, whereas an assumed 18% peak shaving case lowers it to about 100.5 MW and indicates 22.1 MW of gross reinforcement deferral. The results show why port capacity planning needs a validated hourly representation of operational events, explicit long-term pathways and uncertainty statements appropriate to each horizon.

Prediction of heat demand using artificial neural networks has attracted enormous research attention. Weather conditions, such as direct solar irradiance and wind speed, have been identified as key parameters affecting heat demand. This paper employs an Elman neural network to investigate the impacts of direct solar irradiance and wind speed on the heat demand from the perspective of the entire district heating network. Results of the overall mean absolute percentage error (MAPE) show that direct solar irradiance and wind speed have quite similar impacts. However, the involvement of direct solar irradiance can clearly reduce the maximum absolute deviation when only involving direct solar irradiance and wind speed, respectively. In addition, the simultaneous involvement of both wind speed and direct solar irradiance does not show an obvious improvement of MAPE. Moreover, the prediction accuracy can also be affected by other factors like data discontinuity and outliers.

This paper describes the results from implementing an energy intervention framework, a novel method to evaluate consumers' willingness to participate in demand-response actions. An energy intervention scenario encouraged consumers to change consumption behaviour during peak hours in December. Consumers' participation was measured through a web-based survey as well as using smart meter data. The study included 528 consumers divided into three groups: i) participation with economic compensation; ii) participation without economic compensation; and iii) control group. In total 106 households responded to the survey, 53 stated that they actively took part in the energy intervention. When analysing smart meter data it was evident that the groups that had received the intervention and confirmed their participation had lowered the load compared to the control group by 19%. The monetary incentive did not have a positive effect on either participation rate or reduced consumption. None of the participants claimed the economic compensation.

The use of ground-sourced heat pumps as main heating systems has increased in Sweden in the last fifteen years to the point that it is the country with the highest amount of GSHP in Europe. Heat pumps are chosen by many households due to their economic savings value; In contrast, electricity prices in Sweden have almost doubled since 2006, threatening their economic benefits. It is therefore, essential to understand GSHPs impact on the user's electricity consumption and provide suitable demand-response programs that could help develop a model capable of forecasting consumption and provide decision support information to make the best use of the technology. This paper analyses questionnaire surveys and consumption patterns were evaluated for 322 households with installed GSHPs and different pricing schemes in order to increase the understanding of mass use of this type of heating system.