HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings

Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

Temperature Control System Using Mobile Application Interface

Temperature control becomes increasingly necessary with each day, be it in industrial, commercial or residential environments. In a similar way, technology has become a common tool in everyday life. Thus, the technologies need to accompany advances in the controls of processes in many fields, creating applications that can be used in mobile devices alongside these processes. Therefore, this paper proposes temperature monitoring of an environment, via Bluetooth wireless communication and with interface display on a mobile application, developed in MIT App Inventor. While implementing commands via hardware and software a procedure to lower room temperature was applied through a ventilation system. The data are collected through a DHT11 temperature sensor, and the wireless communication is through a HC-05 Bluetooth module, both connected to the development board Arduino. It was possible to condition the cooler to work accordingly with a preset temperature range by using its IDE (Integrated Development Environment). Thereby, this project is a low-cost and advantageous alternative to temperature control and monitoring supported by technological advancements.

Recurrent Neural Network-Based Temperature Control System Weight Pruning Based on Nonlinear Reconstruction Error

Recurrent Neural Networks (RNNs) have been widely applied in various fields. However, in real-world application, because most devices like mobile phones are limited to the storage capacity when processing real-time information, an over-parameterized model always slows down the system speed and is not suitable to be employed. In our proposed temperature control system, the RNN-based control model processes the real-time temperature signals. It is necessary to compress the trained model with acceptable loss of control performance for further implementation in the actual controller when the system resource is limited. Inspired by the layer-wise neuron pruning method, in this paper, we apply the nonlinear reconstruction error (NRE) guided layer-wise weight pruning method on the RNN-based temperature control system. The control system is established based on MATLAB/Simulink. In order to compress the model size to save the memory capacity of temperature controller devices, we first prove the validity of the proposed reference-model (ref-model) guided RNN model for real-time online data processing on an actual temperature object; relative experiments are implemented based on a digital signal processor. On this basis, we then verified the NRE guided layer-wise weight pruning method on the well-trained temperature control model. Compared with the classical pruning method, experiment results indicate that the pruned control model based on NRE guided layer-wise weight pruning can effectively achieve the high accuracy at targeted sparsity of the network.

Research on Energy-Saving Optimization of Rural Houses in Specific Regions Based on Internet of Things

With the expansion of social energy use, the optimization of residential energy conservation has become urgent and important. The research on rural housing in areas with hot summers and cold winters started late and is relatively backward in terms of housing energy saving, which greatly hinders the sustainable development of rural areas. The application of the Internet of Things technology helps to provide a stable technical guarantee for energy-saving optimization. Therefore, this paper takes the energy-saving optimization design of rural houses in hot summer and cold winter areas based on the Internet of Things technology as the research theme. This article first takes H rural area as the research object and analyzes the climatic conditions of the place, that is, the typical characteristics of hot summer and cold winter. Then, the intelligent temperature acquisition system is designed, and the working process and main hardware modules of the system are introduced. This text combines GPRS and Internet of Things technology, designs the temperature control system, and carries on the test to the system operation effect. The test results show that during the heating period in January, before and after the temperature control system controls the room temperature, the maximum relative error between the set temperature value and the actual temperature value is 0.49 and 0.27, respectively. It can be seen that the intelligent temperature energy-saving control system can collect the indoor temperature in real time and can well control the heating equipment.

Active and accurate temperature control of terahertz functional devices via micro-hotplate system

Based on the problem that the intensity of excitation source is not easy to regulate by the traditional active control method, this paper presents an accurate temperature control system based on micro-hotplate for the first time. This system realizes the active control of terahertz metamaterial functional devices, and implements various functions by using the proposed accurate temperature control process. The temperature control characteristics of micro-hotplate are introduced into the design of terahertz functional devices by taking a vanadium dioxide (VO₂ ) metamaterial absorber as an example. In this design, a silicon-based micro-hotplate is used to heat the metamaterial absorber. According to the phase transition characteristics of VO₂ , the alteration of temperature leads to conductivity change, so as to realize the active control of the absorber. At the same time, this paper also analyzes the heating and cooling time of the micro-hotplate. The simulation results show that, by using the micro-hotplate to heat the metamaterial functional devices, the temperature adjustment speed is reasonably high and the controllable performance is excellent. The test results shows that the surface temperature can be controlled between 40 ℃ and 80 ℃ , and the temperature difference of the working area can be kept within 1℃ . The temperature control of the micro-hotplate is accurately controlled, resulting in the great performance of the metamaterial functional devices.

Intelligent Agricultural Automatic Control System Based on Internet of Things

The advanced technology of the Internet and extensive agricultural transformation and upgrading have made the overall agricultural industry chain and modern agriculture better. In view of the high cost and suffering of traditional agricultural planting management, the Internet of Things (I O T) is applied to agriculture to realize real-time detection and intelligent management of crop growth conditions, remote control, and change the traditional agricultural equipment planting mode. The purpose of this article is to design and research the I O T automatic control (A C) system for smart agriculture. This article first introduces the core technology of the I O T through an overview of the basic theories of the I O T. Combined with the current status of agricultural automation in my country, the existing problems and deficiencies are analyzed. On this basis, use the core technology of the I O T to supplement and improve it. This article systematically expounds the overall scheme design, module function design and A C algorithm realization of the I O T brake control system. And use field investigation method, comparative analysis method and other research forms to carry out research on the theme of this article. Experimental research shows that the sampling data of the greenhouse is selected as the sample, the appropriate initial membership function is selected, and the fuzzy control rules obtained through the training of the fuzzy neural network algorithm are relatively correct. The output result of the automatic temperature control system is mostly consistent with the actual data on site. Overall, the temperature A C system designed in this subject can meet the A C requirements of agriculture.

Automatic Temperature Control System on Smart Poultry Farm Using PID Method

Chicken farmers in Indonesia are facing a problem as a result of the country's harsh weather conditions. Poultry species are very susceptible to temperature and humidity fluctuations. As a result, an intelligent poultry farm is necessary to intelligently adjust the temperature in the chicken coop. A smart poultry farm is a concept in which farmers may automatically manage the temperature in the chicken coop, thereby improving the livestock's quality of life. The purpose of this research is to develop a chicken coop prototype that focuses on temperature control systems on smart poultry farms via the PID control approach. The PID control method is expected to allow the temperature control system to adapt to the temperature within the cage, thereby assisting chicken farmers in their tasks. The sensor utilized is a DHT22 sensor with a calibration accuracy of 96.88 percent. The PID response was found to be satisfactory for the system with Kp = 10, Ki = 0, and KD = 0.1, and the time necessary for the system to reach the specified temperature was 121 seconds with a 1.03 % inaccuracy.