Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.

No abstract

The object of the study is samples of food powders obtained by grinding the products of the collection and processing of a number of grain crops, using air-grinding technology in an improved jet mill. One of the most important problems of the modern food industry is that for the production of flour from cereals endosperm is used while the most important nutrients are found in shells and the germ of the grain. As a result of its grinding in conventional mills, common at existing mills, large pieces of bran and a large variation in the particle size of the grinding products are obtained, and this method is energy-intensive. According to the authors, the best solution to ensure truly whole grain grinding – that is, grinding grains with shells – is air grinding in jet mills. An improved jet mill makes it possible to grind both endosperm and grain shells into flour of the same consistency. From the same amount of raw material, therefore, it is possible to produce approximately 30 % more final grinding products. It is also important that the improved jet mill, under proven conditions, spends no more energy for grinding than a conventional mill. For research, the most popular products ground in such a mill were taken – wheat flour (black grain), buckwheat flour (from roasted buckwheat) and wheat bran. The first two products are whole grain milled, and the bran is produced from the collapse of wheat grown in accordance with the requirements of organic farming. Samples of powders obtained by grinding these products in an improved jet mill were compared with control samples – produced from similar raw materials in a roller mill – the most common design in service with mills. Physical indicators of the powders, thermophysical properties and biotechnological parameters were carried out. The obtained results allow to state that whole grain grinding produced on an improved jet mill has the characteristics better or close to standard types of flour produced on conventional mills. It allows them to be used without significant changes in the formulation of products with their addition (bakery, pasta, etc.), and also to create new dietary, healthy products rich in biologically active substances.