Predicting indoor air temperature and thermal comfort in occupational settings using weather forecasts, indoor sensors, and artificial neural networks

Smart district heating system in smart town

This paper describes a microclimate monitoring system consisting of a LoRaWAN network of wireless climate sensors, a data collector and analytical software. The system is a part of the ICS RAS SmartGrid Centre project for predicting building energy consumption. During the design phase, the authors considered the concept of comfort, which is involved in setting control objectives for HVAC plants. It was necessary to overcome some characteristics of the LoRaWAN protocol, such as floating data transmission period and limited intensity of sensor communication. These have been overcome by post-processing the data with Python software, using libraries numpy and scipy. The collected data was passed through an interpolation filter for synchronization, and the resulting data is freely available in dataset format on our website for all interested researchers. Additionally, weather data was collected using a local meteostation to be considered as external disturbances in analysis problems. This paper also considers an approach to passive identification of the thermal protection parameters of a building. The coronavirus lockdown period was chosen to assume the impact of visitors negligible. The parameters are supposed to be estimated by correlation analysis. The estimates obtained should be compared with the values calculated according to ISO and Russian construction standards for diagnostic reasons. 

Microclimate Monitoring System Design for the Smart Grid Analysis and Constructive Parameters Estimation

В статье кратко описывается текущее состояние рынков тепловой энергии в странах с развитыми системами централизованного теплоснабжения (страны Северной Европы, Китай и Россия). Показаны изменения в организационной структуре этих рынков в контексте смены поколений в системах централизованного теплоснабжения. В настоящее время в России и за рубежом активно развиваются системы централизованного теплоснабжения 4-го и 5-го поколений. Такие системы смогут обеспечить теплоснабжение зданий с низкими потерями в сети при низком энергопотреблении. Введено понятие «активного потребителя (prosumer)». Разработана обобщенная блок-схема системы автоматизации для активного потребителя. Сделан вывод о необходимости разработки беспроводных сенсорных систем управления централизованного теплоснабжения, отвечающих изменениям рынка. Приведена структурная схема такой системы управления.

Системы централизованного теплоснабжения для умных городов

The article describes a model of the indoor climate control process, as well as structural diagrams of control modules based on neuro-fuzzy modeling. The developed model of intelligent climate control uses the value of the thermal comfort index PMV to control the values of temperature and humidity in the room. A mathematical model and algorithms for this model are presented. A two-layer neural network will allow you to predict the PMV index with sufficient accuracy, especially on data not from the training set. The fuzzy logic module uses a database of 33 rules for converting information from sensors (temperature and humidity) and a neural network into fuzzy information to regulate temperature and humidity values based on the value of the PMV comfort index. To take into account the dependence of the room temperature on the external temperature and the heater temperature, an additional Tdif module was introduced, which allowed to increase the efficiency of the model. Computational experiments have shown that based on this model, it is possible to obtain the economic efficiency of the indoor climate control system.

Intelligent Indoor Microclimate Control

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Intelligent Indoor Microclimate Control