Although there are numerous high performance BMS (Building Management System), which monitor the indoor climate parameters, data access, sensor positioning, and other aspects may not be under control. On the other hand, IoT (Internet of Things) is experiencing exponential growth, as more and more devices and sensors are connected to the cloud. Thus, a sensor monitoring solution for indoor climate parameters was developed. The proposed solution is not expensive, and it is based on a Raspberry Pi board endowed with temperature, humidity, and pressure sensors. The developed application reads the values detected by the sensors, processes the date, and afterwards transmit the information to the IoT ThingSpeak platform. The large area is characteristic of open space offices, so the influence of radiant walls is small, and the operative temperature can be approximate with air temperature. This type of building is conditioned by the air conditioning system, so the air speed in this indoor environment is usually low and could be approximated by the design. So, with the data read by the developed solution the thermal comfort parameters can be approximated. If inadequate values are found, teams that carry out complex and precise measurements could be sent to the site. To achieve this goal a PMV calculator software is developed. Its validity is tested in accordance with the European standard ISO 7730. After that, the PMV computer is used with data read from sensors. Both the data read from the sensors and the newly calculated PMV are sent to the ThingSpeak IoT platform.

The global trend is to increase the thermal comfort in all kinds of buildings, residential and non-residential. At the same time, minimization of energy consumption and improved building sustainability must be achieved. Only precise calculations can keep the balance between maximizing comfort and minimizing energy consumption. The PassivHaus (PH) is a special type of building with low heating energy consumption that fulfills special requirements defined by the Passivhaus Institut of Darmstadt, Germany. The PassivHaus concept has been successfully implemented in climates other than that of Germany. However, current practice in Southern European countries shows that PHs may exhibit overheating in the hot season, a phenomenon which is not very often encountered in Germany. Shading devices may be considered in these southern countries to improve the thermal comfort in PHs during summer. A model of the AMVIC PH office building located near Bucharest, Romania, has been developed using Simergy and EnergyPlus software. The model was validated and calibrated using measured data during summer. Measurements have been used to estimate the classical thermal comfort indices such as predicted mean vote and predicted percent of dissatisfied. Simulations have been performed to study the effect on thermal comfort of several shading devices, such as exterior blinds, exterior shades, and overhangs. Exterior blinds and exterior shades are generally more efficient than overhangs when the results are discussed with reference to transmitted solar energy flux through the windows.

National and international adaptive thermal comfort (ATC) standards gain now wider acceptance. These standards propose adaptive comfort equations derived from specific databases. New ATC equations are developed worldwide, under different latitudes and climates. A field survey has been performed in 2013–2014 in six naturally ventilated buildings located in Bucharest (Romania, Köppen type D - temperate continental climate). Comfort parameters were measured and comfort questionnaires were distributed. Using the theory of ASHRAE 55 Standard an ATC equation specific to Romanian climate has been proposed for the first time. Also, the 80% and 90% acceptability bands were determined in this situation. The new ATC equation has been compared with other ATC equations found in literature for similar climates. New thermal comfort investigations have been performed one year later after the first survey, new comfort investigations were performed. The ATC equation has been checked and good agreement has been found.

Adaptive thermal comfort refers to the ability that the occupants of a building have to accustom themselves to specific thermal conditions. The notion of human thermal comfort (HTC) is therefore introduced and defined by a simple mathematical equation. In the evaluation process of actual buildings and in the design of new ones, adaptive HTC equations translate into a substantial reduction in energy consumption and maintenance costs to the buildings. Specialised databases provide adaptive HTC equations that are regarded as standard over large geographical areas. However, in particular climate regions, new adaptive HTC equations have to be established. This paper aims to find an HTC equation specific to the Romanian climate (Köppen climate type D – temperate continental climate). In order to achieve that, a field survey was conducted between 2013 and 2014 in several naturally ventilated buildings (two university buildings from Bucharest, a passive office building and a residential house). The input data consist of questionnaire surveys as well as measurements of comfort parameters.

The market for smart greenhouses has been valued at USD 1.77 billion in 2022 and is expected to grow to 3.39 billion by 2030. In order to make this more efficient, with the help of Internet of Things (IoT) technology, it is desired to eliminate the problem of traditional agriculture, which has poor monitoring and accuracy control of the parameters of a culture. Climate control decisions in a greenhouse are made based on parameter monitoring systems, which can be remotely controlled. Instead of this adjustment of the measured parameters, it would be preferable from the point of view of energy consumption that they should be calculated at optimal values from the design phase of the greenhouse. For this reason, it would be better to perform an energy simulation of the greenhouse first. For the study carried out in this work, a small greenhouse (mini-greenhouse) was built. It was equipped with an IoT sensor system, which measured indoor climate parameters and could send data to the cloud for future recording and processing. A simplified mathematical model of the heat balance was established, and the measured internal parameters of the mini-greenhouse were compared with those obtained from the simulation. After validating the mathematical model of the mini-greenhouse, this paper aimed to find the optimal position for placing a normal-sized greenhouse. For this, several possible locations and orientations of the greenhouse were compared by running the mathematical model, with which the most unfavorable positions could be eliminated. Then, some considerably cheaper “mini-greenhouses” were made and placed in the locations with the desired orientations. Using sensor systems and technologies similar to those presented in this work, the parameters from all mini-greenhouses can be monitored in real time. This real-time monitoring allows for the simultaneous analysis of all greenhouses, without the disadvantages of data collection directly in the field, with all data being recorded in the cloud and other IoT-specific advantages being made use of. In the end, we can choose the optimal solution for the location of a real-size greenhouse.

The aim of this paper is to develop an advanced system of indoor parameters monitoring and management to increase operational efficiency and improve the life in buildings. The system will monitor the environmental parameters, using a system of sensors for temperature, relative humidity, and the main indoor pollutants measurement. Reports related to the building’s CAD plans are defined. Thus, for each room, the corresponding indoor parameters can be viewed in real time. In addition, using the read parameters a heat index is computed by several methods. The solution helps to locate the problems with certainty, due to the relationship with the CAD plans. It could support the facilities managers by alerting maintenance teams when the parameters exceed certain values. The sensor system is cheap, and its scalability allows the management of an unlimited number of objectives.