Electricity is a quite unique commodity. Due to the economically non-storable nature of the commodity that electricity is, the constant balance between consumption and production, weather effects, such as temperature, wind speed, solar intensity etc, and the intensity of everyday and business activities, e.g. holi- days, weekends, on- and off-peak hours etc., the price dynamics of this commodity are quite unique as they are not observed in any other market. This extreme price volatility has forced market participants to hedge volumes as well as price risks. Naturally, electricity price forecast models are of great interest to portfolio managers.
Current data-driven models are combined with expertise of traders due to the considerable uncertainty of these models. We aim to subject trader expertise to a transparent methodology using structured expert judgement. During this study, we conduct two elicitations whose variables of interest concern average day- ahead spot prices for 2025, 2030 and 2035. We distinguish between baseload and peakload price. The first elicitation uses assessments regarding current Dutch day-ahead electricity spot prices to forecast forecast the variables of interest. We forecast the average day-ahead electricity spot price for 2025, 2030 and 2035. The second elicitation uses assessments regarding data on past, current and future developments in the Dutch electricity markets to forecast forecast the variables of interest. The second elicitation results in a decision maker with a higher price forecast than the decision maker of the first elicitation. Uncertainty regarding the forecast is higher for 2025 and 2030 during the second study. Uncertainty is equally high regarding the peakload price forecast for 2035 in both studies, however the range shifts.

We inspect the behavior of the probability that a weighted sum of random variables with log-normal tails is greater than its expected value. Under the right conditions for the weights and the variance being set to 1; we were able to bound a suitable transformation of this probability with the upper bound being a fixed factor of the square root of e above the lower bound. Beyond this, we analyse the conditions on the weights and determine a method for letting the weights be random and give an example.
We end off by extending our result to general variance, where we see that the deviation between the lower and upper bound as well as the domain for the result are dependant on the variance.

Porous asphalt resides on most top layers of Dutch roads. Scheduling maintenance for these roads is generally dependent on several factors, but ravelling, the loss of aggregates in the top layers, is the main reason for maintenance on Dutch roads. With the recent framework of the DOS-LCMS scheme generating values for aggregate loss in percentages, a prediction for the remaining lifetime of a road section surfaced with porous asphalt with respect to ravelling can be performed. The lifespan for porous asphalt layers is dependent on the most suffered 25% of the section on the respective 100 meter length.
The current threshold is set at 10%, implying that road sections of 100 meter need maintenance if more than 25% of the road (75^th percentile) measures aggregate loss over 10%. The present work approximates these 75^th percentiles throughout the years using parametric and non-parametric approaches, whereafter the estimates of the 75^th percentiles are used to construct smooth monotonic increasing convex curves. These curves, which are in fact functions built on P-splines, are then used to perform extrapolation and hence predict the dates on which the threshold is going to be surpassed. The study reveals problems in the raw data which is particularly prominent in the sequence of 75^th percentiles, frequently showing a lack of monotonicity and convexity. Putting the monotonicity and convexity constraints on a more granular level were found to be helpful for the predictions and improved the consistency of lifetime predictions over consecutive years.