Wind is a highly unstable renewable energy source. Accurate forecasting can mitigate the effects of wind inconsistency on the electric grid and help avoid investments in costly energy storage infrastructure. Basing the predictions on open‐source forecast models and climate data also makes them entirely free of charge. The present work studies the feasibility of using two machine learning (ML) models and one deep learning (DL) model, random forest (RF) regression, support vector regression (SVR), and long short‐term memory (LSTM) for short‐term wind power forecasting based on the publicly accessible ERA5‐Land dataset. For each forecast model, a selection of hyperparameters is first tuned, followed by determining the best performing input data structure using surrounding data grid points and increasing the time interval of data affecting a single prediction. Both the ML models and the DL model perform better than the baseline (BL) model when forecasting wind speed up to 24 hours ahead. However, a reduced forecast duration is needed to achieve satisfactory wind turbine (WT) power output forecast accuracy. Most notably, the RF is able to produce 3‐hour forecasts with the combined WT power output prediction error amounting to less than 10 % of the WT's nominal power.

Measuring friction between the tyres of a vehicle and the road, often and on as many locations on the road network as possible, can be a valuable tool for ensuring traffic safety. Rather than by using specialised equipment for sequential measurements, this can be achieved by using several low-cost measuring devices on vehicles that travel on the road network as part of their daily assignments. The presented work proves the hypothesis that a low cost measuring device can be built and can provide measurement results comparable to those obtained from expensive specialised measuring devices. As a proof of concept, two copies of a prototype device, based on the Raspberry Pi single-board computer, have been developed, built and tested. They use accelerometers to measure vehicle braking deceleration and include a global positioning receiver for obtaining the geolocation of each test. They run custom-developed data acquisition software on the Linux operating system and provide automatic measurement data transfer to a server. The operation is controlled by an intuitive user interface consisting of two illuminated physical pushbuttons. The results show that for braking tests and friction coefficient measurements the developed prototypes compare favourably to a widely used professional vehicle performance computer.

Emerging applications in fields such as extended reality require both a high throughput and low latency. The millimeter-wave (mmWave) spectrum is considered because of the potential in the large available bandwidth. The present work studies mmWave Wi-Fi physical layer latency management mechanisms, a key factor in providing low-latency communications for time-critical applications. We calculate physical layer latency in an ideal scenario and simulate it using a tailor-made simulation framework, based on the IEEE 802.11ad standard. Assessing data reception quality over a noisy channel yielded latency’s dependency on transmission parameters, channel noise, and digital baseband tuning. Latency in function of the modulation and coding scheme was found to span 0.28–2.71 ms in the ideal scenario, whereas simulation results also revealed its tight bond with the demapping algorithm and the number of low-density parity-check decoder iterations. The findings yielded tuning parameter combinations for reaching Pareto optimality either by constraining the bit error rate and optimizing latency or the other way around. Our assessment shows that trade-offs can and have to be made to provide sufficiently reliable low-latency communication. In good channel conditions, one may benefit from both the very high throughput and low latency; yet, in more adverse situations, lower modulation orders and additional coding overhead are a necessity.

Putting glass doors on the display cases of refrigerators is one of the most efficient ways to reduce the energy consumption of supermarkets. However, the glass fogs up when opening the door because of the difference in air temperature inside and outside of the refrigerator, thereby obscuring the view. To defog the glass, anti-sweat heaters (ASHs) are used. In this paper, the power usage of ASHs according to changes in the dew point (DP) inside a supermarket were evaluated for two types of ASH, i.e., the door-frame ASH and the glass ASH. The evaluation was based on measurements of the condensation on the glass doors of vertical display cases, used for the preservation of frozen foodstuffs. A mathematical model of the correlation between the ASH’s power usage and the DP was developed and used for predicting the long-term energy savings. The savings were calculated based on the measured DPs inside the supermarket, which were extrapolated over a longer time period based on their correlation with the outside DPs. Regulating the door-frame ASH according to the DP resulted in an 84.6% reduction in energy consumption and a 90.1% reduction in the case of the glass ASH, compared to the current state. The correlation between the DPs inside and outside the supermarket served as a basis for the proposed implementation of the power usage regulation of the ASH according to the DP.

Millimeter-wave (mmWave) technology holds the potential to revolutionize head-mounted displays (HMDs) by enabling high-speed wireless communication with nearby processing nodes, where complex video rendering can take place. However, the sparse angular profile of mmWave channels, coupled with the narrow field of view (FoV) of patch-antenna arrays and frequent HMD rotation, can lead to poor performance. We introduce six channel performance metrics to evaluate the performance of an HMD equipped with mmWave arrays. We analyze the metrics using analytical models, discuss their impact for the application, and apply them to 28 GHz channel sounding data, collected in a conference room using eight HMD patch-antenna arrays, offset by 45° from each other in azimuth. Our findings confirm that a single array performs poorly due to the narrow FoV, and featuring multiple arrays along the HMD’s azimuth is required. Namely, the broader FoV stabilizes channel gain during HMD rotation, lessens the attenuation caused by line of sight (LoS) obstruction, and increases the channel’s spatial multiplexing capability. In light of our findings, we conclude that it is imperative to either equip the HMD with multiple arrays or, as an alternative approach, incorporate macroscopic diversity by leveraging distributed access point (AP) infrastructure.

Europe’s massive shift towards sustainable energy production has triggered a variety of new large scale projects, and wind energy is a crucial part of the effort to achieve a carbon-free future. However, because of their low financial impact and relatively high measurement campaign costs, small scale projects are often deemed impractical beforehand. To help small communities gain insight on the wind energy conditions in their surroundings, the present work briefly introduces a measuring station (MEST) concept based on affordable electronic components and proposes a solution to alleviating the effects of inevitable measurement data inconsistency on the energy yield analysis. By leveraging open source machine learning models and establishing a link with the publicly available ERA5-Land climate database, missing wind speed measurement data is reconstructed at an accuracy of up to 0.11 $\frac{m}{s}$. The impact of data reconstruction on the estimated energy production of a wind turbine (WT) erected at the measuring location is then evaluated using the measurement data acquired by a MEST prototype and the ERA5-Land data recorded during October and November 2019. The results indicate that at a location experiencing moderate wind speeds, the estimated energy output of the WT is increased by up to 2 % in comparison with other data analysis procedures. Although the minute underestimation is not of great importance to the success of the analysis, the inaccuracies at higher wind speeds have a far more profound effect on the WT’s estimated energy output, and they can stop a potentially successful wind energy project from gaining further attention.