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IntroductionConvenience foods are a double-edged sword in that they provide quick and easy nutrition but may promote non-communicable diseases related to excess intakes of sugar, fat, and salt. To inform the German national reduction and innovation strategy for less sugar, fat, and salt in processed foods, the present study sought to analyse the consumption frequency of selected convenience foods and to determine sociodemographic and behavioural factors that characterise frequent users.MethodsIn a representative computer-assisted telephone interview survey in the adult German population (N = 3,997) conducted in 2018, consumption frequency of 21 convenience foods was assessed. To characterise frequent in contrast to non-frequent users, data on sociodemographics and behavioural aspects were compared. Statistical analyses comprised chi-square tests with Bonferroni correction as well as Spearman’s rank correlation. Cramer’s V was used to determine the strength of an association.ResultsOverall and among frequent users (7.7% of the sample) sweet convenience foods and savoury cooking aids were consumed most frequently. Around 75% of the participants indicated little-to-no consumption of 19 of the 21 convenience foods. Male gender (p

Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.

High resolution irradiance forecasts based on sky imagers are valuable for applications that require short term decisions based on ramps of solar irradiance. Here, we present our sky imager based forecasting algorithm, using images of a low cost surveillance camera. Model development and evaluation is done separately for the different steps in sky imager forecasting, starting with cloud detection, followed by estimation and extrapolation of cloud movement, and finally deriving irradiance forecasts from the predicted cloud images. We distinguish between clear and cloudy conditions and especially evaluate the effect of cirrus situations on the different forecasting steps. To create binary cloud masks, we adapted a pixel value based cloud algorithm using a set of manually classified pixels. In an independent validation dataset 90.3 % of the pixels are classified correctly. For the circumsolar region, where cloud decision is known to be especially challenging and crucial for the forecasting of the direct component of the solar radiation, we introduce a correction procedure using real-time irradiance measurements and object recognition methods. Applying this method we can significantly improve the cloud detection in the circumsolar region and increase the forecast skill of the cloud decision forecast. The development of the irradiance algorithm is a special focus of this paper. Real-time irradiance measurements and cloud decision information are used as input to our irradiance model. The algorithm is developed using cloud decision information derived from measurements instead of sky imager cloud decision forecasts to exclude the influence of errors in cloud decision and cloud motion methods for model development. Afterwards, the irradiance algorithm is applied to sky imager based cloud decision forecasts. Even though we start with binary cloud information, the distribution of the clear sky index from our forecasts is in very good agreement with the distribution of the measurements. In a validation dataset of 46 days, we receive a positive forecast skill for all forecast horizons larger than 1 min. We also apply our forecast chain to a dataset of two month from an independent measurement station resulting in a comparable forecasting performance.