Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems

Globally, the buildings sector accounts for 30% of the energy consumption and more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and Air Conditioning (HVAC) system is the most extensively operated component and it is responsible alone for 40% of the final building energy usage. HVAC systems are used to provide healthy and comfortable indoor conditions, and their main objective is to maintain the thermal comfort of occupants with minimum energy usage. HVAC systems include a considerable number of sensors, controlled actuators, and other components. They are at risk of malfunctioning or failure resulting in reduced efficiency, potential interference with the execution of supervision schemes, and equipment deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve their reliability, efficiency, and performance, and to provide preventive maintenance. In this thesis work, two neural network-based methods are proposed for sensor and actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised sensor data validation and fault diagnosis method using an Auto-Associative Neural Network (AANN) is developed. The method is based on the implementation of Nonlinear Principal Component Analysis (NPCA) using a Back-Propagation Neural Network (BPNN) and it demonstrates notable capability in sensor fault and inaccuracy correction, measurement noise reduction, missing sensor data replacement, and in both single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks (CNNs) is developed for single actuator faults. It is based a data transformation in which the 1-dimensional data are configured into a 2-dimensional representation without the use of advanced signal processing techniques. The CNN-based actuator fault diagnosis approach demonstrates improved performance capability compared with the commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and standard Neural Networks). The presented schemes are compared with other commonly used HVAC fault diagnosis methods for benchmarking and they are proven to be superior, effective, accurate, and reliable. The proposed approaches can be applied to large-scale buildings with additional zones

Monitoring and control for NGL recovery plant

The thesis explores the production of natural gas liquids (NGL) and the challenge of monitoring and controlling the fractionation process. NGLs are the C2+ hydrocarbon fraction contained in natural gas, which includes useful feedstocks for industrial production processes. Since NGLs have greater economic value compared to natural gas, their recovery has become increasingly economically significant, leading to a need for efficient fractionation. This energy-intensive process is typically conducted in separation trains that include cryogenic distillation columns. Given the high cost of composition analyzers and the related significant delays, this work proposes the use of only indirect composition control strategies, as well as data-driven control strategies to achieve the desired product quality and optimize the plant energy consumption under typical disturbances. Feedforward neural networks (FFNs) were used for the development of soft sensors used in data-driven control schemes. Given the multitude of data made available by the process simulator, this work aims to develop a demethanizer digital twin that can approximate the column dynamics with reduced computation time. Long Short-Term Memory neural networks (LSTM), along with physical knowledge, were used to develop different neural network architectures compared to select the most suitable for the surrogate model development. Realistic measurement noises were considered to accurately reflect the measurements of real industrial plants and only easy-to-measure variables were used as input data for the developed neural model. Overall, the research presents an energy-efficient NGL recovery offering a cost-effective and efficient alternative to traditional measuring instruments. Moreover, the study illustrates a novel application of LSTM for distillation columns digital twins realization, providing a useful tool for optimization, monitoring and control by employing available plant measurements

An Iterative Optimization and Learning-based IoT System for Energy Management of Connected Buildings

Buildings account for nearly 40% of primary energy and 36% of greenhouse emissions, which is one of the main factors driving climate change. Reducing energy consumption in buildings toward zero-energy buildings is a vital pillar to ensure that future climate and energy targets are reached. However, due to the high uncertainty of building loads and customer comfort demands, and extremely nonlinear building thermal characteristics, developing an effective zero-energy building energy management (BEM) technology is facing great challenges. This paper proposes a novel learning-based and iterative IoT system to address these challenges to achieve the zero-energy objective in BEM of connected buildings. Firstly, all buildings in the IoT-based BEM system share their operation data with an aggregator. Secondly, the aggregator uses these historical data to train a deep reinforcement learning model based on the Deep Deterministic Policy Gradient method. The learning model generates pre-cooling or pre-heating control actions to achieve zero-energy BEM for building heating ventilation and air conditioning (HVAC) systems. Thirdly, for solving the coupling problem between HVAC systems and building internal heat gain loads, an iterative optimization algorithm is developed to integrate physics-based and learning-based models to minimize the deviation between the on-site solar photovoltaic generated energy and the actual building energy consumption by properly scheduling building loads, electric vehicle charging cycles and the energy-storage system. Lastly, the optimal load operation scheduling is generated by considering customers’ comfort requirements. All connected buildings then operate their loads based on the load operation schedule issued by the aggregator. The proposed learning-based and iterative IoT system is validated via simulation with real-world building data from the Pecan Street project

Transient stability assessment of hybrid distributed generation using computational intelligence approaches

Includes bibliographical references.Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, power electronic interface circuits, advanced controllers etc., the present power system network is now more complex than in the past. Consequently, the recent rate of blackouts recorded in some parts of the world indicates that the power system is stressed. The real time/online monitoring and prediction of stability limit is needed to prevent future blackouts. In the last decade, Distributed Generators (DGs) among other technologies have received increasing attention. This is because DGs have the capability to meet peak demand, reduce losses, due to proximity to consumers and produce clean energy and thus reduce the production of CO₂. More benefits can be obtained when two or more DGs are combined together to form what is known as Hybrid Distributed Generation (HDG). The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy source DG. This is because of the intermittent nature of the renewable energy sources and the complex interaction between the DGs and the distribution network. Due to the complexity and the stress level of the present power system network, real time/online monitoring and prediction of stability limits is becoming an essential and important part of present day control centres. Up to now, research on the impact of HDG on the transient stability is very limited. Generally, to perform transient stability assessment, an analytical approach is often used. The analytical approach requires a large volume of data, detailed mathematical equations and the understanding of the dynamics of the system. Due to the unavailability of accurate mathematical equations for most dynamic systems, and given the large volume of data required, the analytical method is inadequate and time consuming. Moreover, it requires long simulation time to assess the stability limits of the system. Therefore, the analytical approach is inadequate to handle real time operation of power system. In order to carry out real time transient stability assessment under an increasing nonlinear and time varying dynamics, fast scalable and dynamic algorithms are required. Transient Stability Assessment Of Hybrid Distributed Generation Using Computational Intelligence Approaches These algorithms must be able to perform advanced monitoring, decision making, forecasting, control and optimization. Computational Intelligence (CI) based algorithm such as neural networks coupled with Wide Area Monitoring System (WAMS) such as Phasor Measurement Unit (PMUs) have been shown to successfully model non-linear dynamics and predict stability limits in real time. To cope with the shortcoming of the analytical approach, a computational intelligence method based on Artificial Neural Networks (ANNs) was developed in this thesis to assess transient stability in real time. Appropriate data related to the hybrid generation (i.e., Solar PV, wind generator, small hydropower) were generated using the analytical approach for the training and testing of the ANN models. In addition, PMUs integrated in Real Time Digital Simulator (RTDS) were used to gather data for the real time training of the ANNs and the prediction of the Critical Clearing Time (CCT)

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Short-term forecasting for the electrical demand of Heating, Ventilation, and Air Conditioning systems

The heating, ventilation, and air conditioning systems (HVAC) of large scale commercial and institutional buildings can have significant contributions to the buildings overall electric demand. During periods of peak demand, utilities are faced with a challenge of balancing supply and demand while the system is under stress. As such, utility companies began to operate demand response programs for large scale consumers. Participation in such programs requires the participant to shift their electric demand to off-peak hours in exchange for monetary compensation. In such a context, it is beneficial for large scale commercial and institutional buildings to participate in such programs. In order to effectively plan demand response based strategies, building energy managers and operators require accurate tools for the short-term forecasting of large scale components and systems within the building. This thesis contributes to the field of demand response research by proposing a method for the short-term forecasting for the electric demand of an HVAC system in an institutional building. Two machine learning based approaches are proposed in this work: a component method and a system based method. The component-level approach forecasts the electric demand of a component within the HVAC system (e.g. air supply fans) using an autoregressive neural network coupled with a physics based equation. The system-level approach uses deep learning models to forecast the overall electric demand of the HVAC system through forecasting the electric demand of the primary and secondary system. Both approaches leverage available data from the building automation system (BAS) without the need for additional sensors. The system based forecasting method is validated through a case study for a single building with two data sources: measurement data obtained from the BAS and from an eQuest simulation of the building. The building used as the case study for the work herein consists of the Genomic building of Concordia University Loyola campus

メタヒューリスティクスおよび機械学習を用いた建物・地域エネルギーシステムの最適化に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 大岡 龍三, 東京大学教授 加藤 信介, 東京大学教授 赤司 泰義, 東京大学教授 合原 一幸, 東京大学講師 菊本 英紀University of Tokyo(東京大学

DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS.

DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS