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.
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.
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.
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