Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Pembinaan modul soalan-soalan latihan berjawapan bagi mata pelajaran mekanik tanah

Modul Pembelajaran yang dibina adalah bertujuan untuk membantu pelajar dalam menguasai penyelesaian masalah proses pengiraan bagi mata pelajaran Mekanik Tanah. Mekanik Tanah adalah merupakan salah satu subjek yang memerlukan kemahiran di dalam teknik menjawab soalan-soalan latihan menyelesaikan masalah berdasarkan jalan kira yang lengkap. Kajian dijalankan ke atas penentuan tahap keperluan modul soalan-soalan latihan beijawapan bagi mata pelajaran Mekanik Tanah dari aspek kefahaman pelajar, gaya susunan proses pengiraan, kebolehlaksanaan dan sumber rujukan utama. Rekabentuk pembinaan modul adalah merujuk kepada model kerangka Biggs. Responden yang telah dipilih dalam menentukan tahap keperluan ke atas modul ini dari aspek-aspek di atas adalah terdiri daripada pelejar-pelajar Ijazah Saijana Muda Pendidikan Teknik dan Vokasional, KUiTTHO.Data yang diperolehi dianalisis menggunakan Statistical Packages for Social Science (SPSS) 11.0 for Windows. Data-data yang yang dikumpul dari soal selidik dianalisis menggunakan kaedah analisis statistik deskriptif. Tinjauan hasil dapatan ke atas keputusan analisis menunjukkan bahawa tahap keperluan ke atas keempat-empat aspek di atas adalah tinggi. Ini menunjukkan modul diperlukan. Kebanyakan responden bersetuju bahawa keperluan modul soalan-soalan latihan berjawapan ini adalah pada peratusan yang tinggi. Keputusan purata skor min menunjukkan setiap keperluan iaitu dari aspek kefahaman pelajar, gaya susunan proses pengiraan, kebolehlaksanaan dan sumber rujukan utama adalah pada tahap yang tinggi. Secara Keseluruhan hasil analisis bagi purata min skor menunjukkan (analisis spesifikasi 1 adalah 3.21, spesifikasi 2 ialah 3.32., spesifikasi 3 ialah 3.46 dan spesifikasi 4 ialah 3.48). Secara keseluruhan, pembinaan modul set jawapan ini berjaya memenuhi keperluan pelajar-pelajar Ijazah Sarjana Muda Pendidikan Teknik dan Vokasional yang mengambil mata pelajaran Mekanik Tana

hybridGEOTABS project : MPC for controlling the power of the ground by integration

GEOTABS is an acronym for a GEOthermal heat pump combined with a Thermally Activated Building System (TABS). GEOTABS combines the use of geothermal energy, which is an almost limitless and ubiquitous energy source, with radiant heating and cooling systems, which can provide very comfortable conditioning of the indoor space. GEOTABShybrid refers to the integration of GEOTABS with secondary heating and cooling systems and other renewable and residual energy sources (R2ES), offering a huge potential to meet heating and cooling needs in office buildings, elderly care homes, schools and multi-family buildings throughout Europe in a sustainable way. Through the use of Model Predictive Control (MPC), a new control-integrated building design procedure and a readily applicable commercial system solution in GEOTABShybrid, the overall efficiency of heating and cooling will be significantly improved in comparison to current best practice GEOTABS systems and its competitiveness will be strengthened. The present paper is the first of a series that first introduces the hybridGEOTABS project and then specifically focuses on the control-related aspects of the hybridGEOTABS solution, the MPC, providing some interesting insights of its potential development

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Demand side load control in residential buildings with HVAC controller for demand response

Demand Response (DR) is a key factor to increase the efficiency of the power grid and has the potential to facilitate supply-demand balance. Demand side load control can contribute to reduce electricity consumption through DR programs. Especially, Heating, Ventilating and Air Conditioning (HVAC) load is one of the major contributors to peak loads. In the United States, HVAC systems are the largest consumers of electrical energy and a major contributor to peak demand. In this research, the Dynamic Demand Response Controller (DDRC) is proposed to reduce peak load as well as saves electricity cost while maintaining reasonable thermal comfort by controlling HVAC system. To reduce both peak load and energy cost, DDRC controls the set-point temperature in a thermostat depending on real-time price of electricity. Residential buildings are modeled with various internal loads using building energy modeling tools. The weather data in different climate zones are used to demonstrate that DDRC decreases peak loads and brings economic benefit in various locations. In addition, two different types of electricity wholesale markets are used to generate DR signals. To assess the performance of DDRC, the control algorithms are improved to consider the characteristics of building envelopes and HVAC equipment. Also, DDRC is designed to be deployed in various areas with different electricity wholesale markets. The indoor thermal comfort on temperature and humidity are considered based on ASHRAE standard 55. Finally, DDRC is developed to a hardware using embedded system. The hardware of DDRC is based on Advanced RISC Microcontroller (ARM) processor and senses both indoor and outdoor environment with Internet connection capability for DR. In addition, user friendly Graphic User Interface (GUI) is generated to control DDRC.Electrical and Computer Engineerin

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking