The aim of this study was to investigate the pressure agglomeration process of wheat straw (WS) and the blends of WS with calcium carbonate (CC) or cassava straw (CS) with a ratio of 6% wt./wt. from seven separate fractions with sizes in the range of 0.21–2.81 mm. The agglomeration was performed at a moisture of 30% wb and a material temperature of 78 °C, with a dose of 0.1 g, in a die of diameter 8 mm and height 80 mm. The effects of the process were evaluated based on the compaction parameters and the pellets’ density, tensile strength, and water absorption. The incorporation of additives into the WS improved the pellet process and quality. Refined results were achieved after adding CC, as compared to those achieved after adding CS, and the preferred particle size was in the range of 1.00–1.94 mm. This was because, under the given conditions, the back pressure in the die chamber significantly increased, allowing the achievement of a single pellet density of 800 kg·m⁻³. The pellets were resistant to compressive loads and cracked only at tensile strength of 6 MPa and a specific compression work of 6.5 mJ·mm⁻². The addition of CC to the WS improved the strength of the adhesive and the cohesive bonds between the particles. The water absorption for the uncrushed pellets was considerably less than that for crushed pellets, which results in the safer storage of uncrushed pellets and excellent moisture absorption of crushed pellets. The addition of CC to the WS offers benefits in the form of pellet strength with a high water absorption capability. Notably, a study of crushed pellet litter under broiler rearing conditions and an analysis of the operational costs of using WS additives are required for implementing this study.

At present, papers concerning energy consumption and forecasting are predominantly dedicated to various known techniques and their combinations. On the other hand, the research on load modeling and forecasting methodologies is quite limited. This paper presents a new approach concerning hourly energy consumption using a multivariate linear regression model. The proposed technique provides a way to accurately model day-to-day energy consumption using just a few selected variables. The number of data points required to describe a whole day’s consumption depends on the demanded precision, which is up to the user. This model is self-configurable and very fast. The applied model shows that four hours are sufficient to describe energy consumption during the remainder of a given day. We show that for about 84% of the data points, the relative error of the model is below 2.5%, and for all the data points the error does not exceed 7.5%. We obtained a mean relative uncertainty of 1.72% in the learning data set, and 1.69% and 1.82% in the two testing data sets, respectively. In addition, we conclude that the model can also detect days with unusual energy consumption.

In this article, the Leading Point Multi-Regression model was applied to identify days with anomalous energy consumption profiles. The data for the analysis come from the German energy system and they represent the hourly energy demand observed between 2006 and 2015. Days with abnormal daily profiles were identified based on the statistical analysis of the errors observed for the model. The accuracy of the model is very high and comparable with other models, e.g., LSTM, K-means, Recurrent NN, and tree-based ML methods. However, these methods rely on external factors (e.g., humidity, temperature, and sunshine) impacting energy consumption while our model uses only the energy consumption at specific fixed hours, regardless of external factors, thus being universal. Days with anomalous energy consumption profiles were identified as days related to celebration of New Year’s Eve and the New Year. Also, anomalies were identified for some other days, which were not that obvious, including Good Friday, National Day of Mourning, and, interestingly, the day of the Germany–Turkey match during the European Championship in 2008.