A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

open4siSustainable control has begun to stimulate research and development in a wide range of industrial communities particularly for systems that demand a high degree of reliability and availability (sustainability) and at the same time characterised by expensive and/or safety critical maintenance work. For heating systems such as HVAC plants, clear conflict exists between ensuring a high degree of availability and reducing costly maintenance times. HVAC systems have highly non-linear dynamics and a stochastic and uncontrollable driving force as input in the form of intake air speed, presenting an interesting challenge for modern control methods. Suitable control methods can provide sustainable maximisation of energy conversion efficiency over wider than normally expected air speeds and temperatures, whilst also giving a degree of “tolerance” to certain faults, providing an important impact on maintenance scheduling, e.g. by capturing the effects of some system faults before they become serious.This paper presents the design of different control strategies applied to a heating element nonlinear model. The description of this heating element was obtained exploiting a data driven and physically meaningful nonlinear continuous time model, which represents a test bed used in passive air conditioning for sustainable housing applications. This model has low complexity while achieving high simulation performance. The physical meaningfulness of the model provides an enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model based and data driven control designs for tight temperature regulation. The main purpose of this study is thus to give several examples of viable and practical designs of control schemes with application to this heating element model. Moreover, extensive simulations and Monte Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared in order to evaluate advantages and drawbacks of the considered solutions. Finally, the exploited simulation tools can serve to highlight the potential application of the proposed control strategies to real air conditioning systems.openTurhan, T.; Simani, S.; Zajic, I.; Gokcen Akkurt, G.Turhan, T.; Simani, Silvio; Zajic, I.; Gokcen Akkurt, G

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data-driven grey-box identification approach provided the physically–meaningful nonlinear continuous-time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important, since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte Carlo (MC) analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application of the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model-based and data-driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems

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