MLE+: A Tool for Integrated Design and Deployment of Energy Efficient Building Controls

We present MLE+, a tool for energy-efficient building automation design, co-simulation and analysis. The tool leverages the high-fidelity building simulation capabilities of EnergyPlus and the scientific computation and design capabilities of Matlab for controller design. MLE+ facilitates integrated building simulation and controller formulation with integrated support for system identification, control design, optimization, simulation analysis and communication between software applications and building equipment. It provides streamlined workflows, a graphical front-end, and debugging support to help control engineers eliminate design and programming errors and take informed decisions early in the design stage, leading to fewer iterations in the building automation development cycle. We show through an example and two case studies how MLE+ can be used for designing energy-efficient control algorithms for both simulated buildings in EnergyPlus and real building equipment via BACnet

Load Balancing with Energy Storage Systems Based on Co-Simulation of Multiple Smart Buildings and Distribution Networks

In this paper, we present a co-simulation framework that combines two main simulation tools, one that provides detailed multiple building energy simulation ability with Energy-Plus being the core engine, and the other one that is a distribution level simulator, Matpower. Such a framework can be used to develop and study district level optimization techniques that exploit the interaction between a smart electric grid and buildings as well as the interaction between buildings themselves to achieve energy and cost savings and better energy management beyond what one can achieve through techniques applied at the building level only. We propose a heuristic algorithm to do load balancing in distribution networks affected by service restoration activities. Balancing is achieved through the use of utility directed usage of battery energy storage systems (BESS). This is achieved through demand response (DR) type signals that the utility communicates to individual buildings. We report simulation results on two test cases constructed with a 9-bus distribution network and a 57-bus distribution network, respectively. We apply the proposed balancing heuristic and show how energy storage systems can be used for temporary relief of impacted networks

Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices

Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements. This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open-source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state-of-the-art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, preprocessing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community

Exploring Energy, Comfort, and Building Health Impacts of Deep Setback and Normal Occupancy Smart Thermostat Implementation

As smart thermostat adoption rates continue to increase, it becomes worthwhile to explore what unanticipated outcomes may result in their use. Specific attention was paid to smart thermostat impacts to deep setback and normal occupancy states in a variety of conditions while complying with the ventilation and temperature requirements of ASHRAE 90.2-2013. Custom weather models and occupancy schedules were generated to efficiently explore a combination of weather conditions, building constructions, and occupancy states. The custom modeling approach was combined with previous experimental data within the Openstudio graphics interface to the EnergyPlus building modeling engine. Results indicate smart thermostats add the most value to winter deep setback conditions while complying with ASHRAE 90.2. Major potential humidity issues were identified when complying with ASHRAE 90.2 during cooling season. It also appears smart thermostats add little value to occupants when complying with ASHRAE 90.2 during cooling season across multiple climates and building constructions. Further exploration into humidity issues identified are required, as well as refining the energy model and moving towards real-world validation

Optimal Dispatch Controller For Fuel Cell Integrated Building

Buildings contribute to around 40% of the total energy consumption in the US. Improvements to building operation offer substantial economic benefits and emissions reductions. Opportunities arise as more renewable energy sources are integrated into the power grid, where the inherent flexibility that buildings can provide become valuable assets for grid services. Stationary fuel cells providing combined heat and power (CHP) add more flexibility to building operation, where both significant electrical and thermal loads need to be met. As the technology matures, improved fuel cell responsiveness allows for advanced dynamic applications to maximize their utility within the building system. The integration of fuel cells and battery energy storage systems (BESS) to buildings presents several challenges and opportunities for optimal management of resources. In this work, we develop an optimal dispatch controller for real-time management of a fuel cell-integrated building system. The objective is to minimize building operating costs and maximizing profits from participating in the power grid ancillary service markets, while maintaining occupant comfort. To achieve this objective, we develop a specifically tailored model predictive control (MPC) algorithm to schedule the operation of a fuel cell, a BESS, and building equipment in response to the time-of-use electricity tariff. The controller determines the optimal schedules over a 24-hour horizon according to weather and building load forecast. This optimal schedule is implemented for a 1-hour period. Measurements from the fuel cell-integrated building are collected and used to update the optimization for the next 24-hour period. This recursive update ensures that the algorithm is robust to forecast errors and model mismatch. The effectiveness of the proposed algorithm is demonstrated with a co-simulation where the building is represented as a high-fidelity model in the EnergyPlus building simulation program and the optimal control is implemented in Matlab. The proposed optimal dispatch controller provides a tool to manage the real-time operation of a fuel cell-integrated building. It also helps building operators and the fuel cell industry assess the potential benefits of integrating stationary fuel cells with buildings

INDOOR ENVIRONMENTAL QUALITY (IEQ) AND BUILDING ENERGY OPTIMIZATION THROUGH MODEL PREDICTIVE CONTROL (MPC)

This dissertation aims at developing a novel and systematic approach to apply Model Predictive Control (MPC) to improve energy efficiency and indoor environmental quality in office buildings. Model predictive control is one of the advanced optimal control approaches that use models to predict the behavior of the process beyond the current time to optimize the system operation at the present time. In building system, MPC helps to exploit buildings’ thermal storage capacity and to use the information on future disturbances like weather and internal heat gains to estimate optimal control inputs ahead of time. In this research the major challenges of applying MPC to building systems are addressed. A systematic framework has been developed for ease of implementation. New methods are proposed to develop simple and yet reasonably accurate models that can minimize the MPC development effort as well as computational time. The developed MPC is used to control a detailed building model represented by whole building performance simulation tool, EnergyPlus. A co-simulation strategy is used to communicate the MPC control developed in Matlab platform with the case building model in EnergyPlus. The co-simulation tool used (MLE+) also has the ability to talk to actual building management systems that support the BACnet communication protocol which makes it easy to implement the developed MPC control in actual buildings. A building that features an integrated lighting and window control and HVAC system with a dedicated outdoor air system and ceiling radiant panels was used as a case building. Though this study is specifically focused on the case building, the framework developed can be applied to any building type. The performance of the developed MPC was compared against a baseline control strategy using Proportional Integral and Derivative (PID) control. Various conventional and advanced thermal comfort as well as ventilation strategies were considered for the comparison. These include thermal comfort control based on ASHRAE comfort zone (based on temperature and relative humidity) and Predicted Mean Vote (PMV) and ventilation control based on ASHRAE 62.1 and Demand Control Ventilation (DCV). The building energy consumption was also evaluated with and without integrated lighting and window blind control. The simulation results revealed better performance of MPC both in terms of energy savings as well as maintaining acceptable indoor environmental quality. Energy saving as high as 48% was possible using MPC with integrated lighting and window blind control. A new critical contaminant - based demand control ventilation strategy was also developed to ensure acceptable or higher indoor air quality. Common indoor and outdoor contaminants were considered in the study and the method resulted in superior performance especially for buildings with strong indoor or outdoor contaminant sources compared to conventional CO2 - based demand control ventilation which only monitors CO2 to vary the minimum outdoor air ventilation rate

Building comfort control using MPC: Development of a Coupled EnergyPlus-MATLAB Simulation Framework for Model Predictive Control of Integrated Electrical and Thermal Residential Renewable Energy System

The urge of modernizing the building stock in the European Union comes from one clear evidence: it is the largest energy consuming sector, accounting for up to one-third of the total final energy consumption. The vast majority of houses and offices in EU countries were built before 1990 and did not undergo any renovation, meaning they show poor thermal insulation capability, and no smart technique is implemented for the control and reduction of both the electricity and heating demands. This results in significantly high emissions. Almost 40% of EU carbon dioxide emissions indeed come from the building sector [1] , indirectly in the construction process and directly during operation. The set of contaminants also include greenhouse gases such as hydrofluorocarbons, fine particles (PM2.5/PM10) and toxic dusts recognized as one of the main causes of cancer onset [2]. This is precisely related to the fuel mix each country employs to cover the sector needs: 38.2% of the OECD countries residential demand is covered by natural gas and a phasing-out 10% by oil [3]. Technologically advanced solutions such as hydrogen Fuel Cells, integrated with other renewable sources, can represent a clean solution to push down emissions but also energy consumptions by digitalizing the system and implementing control strategies to optimally match demand and generation. This study aims at developing a coupled EnergyPlus-MATLAB Simulation Framework of an integrated electrical and thermal residential renewable energy system with a Model Predictive Controller (MPC) to size and control the operation of a fuel cell stack. A recently renovated single-family house in the province of Turin (IT) is the case-study modelled in EnergyPlus. The simulation requires the building geometry and thermophysical properties and the weather conditions as inputs, and by designing appropriately the controller, a schedule for the heating demand and the resulting evolution of the indoor temperature is obtained.IncomingObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminan

Optimization of building performance via model-based predictive control

Il controllo predittivo basato su modello (MPC) è una tecnica di controllo avanzata che ha svolto un ruolo importante nella gestione di molti processi nel settore industriale. Oggi, nell’ottica di una gestione energetica efficiente degli edifici, l’utilizzo di questa strategia si sta dimostrando una soluzione promettente per ridurre al minimo i consumi e i costi energetici complessivi. Tuttavia, gli studi sulla sua fattibilità tecnica in edifici esistenti sono ancora in una fase iniziale. Pertanto, il risultato principale di questa tesi è la progettazione e lo sviluppo di un prototipo hardware e software per la verifica sul campo di un sistema di controllo predittivo, basato su modello, integrando un modello predittivo virtuale della porzione dell'edificio in esame, il controllore e l'interfaccia grafica per i dispositivi di monitoraggio e regolazione utilizzati. Inoltre, particolare attenzione è stata posta sulla fattibilità tecnica relativa all'implementazione di un tipico sistema MPC, che include un sottosistema di monitoraggio, un set di acquisizione dati e un metodo di identificazione del sistema per ottenere il modello per il controllore, mediante un approccio di modellazione grey-box. La fase di modellazione e l'approccio empirico sviluppato sono presentati nella prima parte di questa tesi di ricerca, mentre la parte centrale riguarda: lo sviluppo del prototipo di controllo predittivo, basato su modello, all'interno di uno strumento virtuale del software LabVIEW e la descrizione del test sperimentale, effettuato durante la stagione di riscaldamento, garantendo la normale operatività dell’edificio durante l'intero periodo di monitoraggio. Infine, è presentato lo studio sviluppato in ambiente di simulazione per indagare il potenziale della logica di controllo per la valutazione di scenari di riqualificazione. Il focus è sulla definizione dei principali componenti del simulatore MPC e sui risultati ottenuti testando uno degli scenari di intervento.Model Predictive Control (MPC) is an advanced control technique which has played an important role in the management of many processes in the industry sector. Nowadays, in the perspective of an efficient building energy management, the exploitation of this strategy is proving to be a promising solution for minimising overall energy consumptions and costs. However, investigations on the feasibility of the technique in real existing buildings are at an initial stage. Hence, the main outcome of this dissertation is the design and development of a prototype hardware and software set up for on-field testing of a model-based predictive control system, integrating a virtual predictive model of the portion of the building under investigation, the controller and the interface to the monitoring and regulation devices used. Moreover, this research is addressed to investigate the technical feasibility of the development and deployment of a typical MPC system, which includes a monitoring sub-system, a data acquisition set up and a system identification method to obtain the model for the controller by means of a grey-box modelling approach. The modelling phase and the empirical approach developed are presented in the first part of this research thesis, while the core part concerns: the development of the MPC prototype, within a virtual instrument of LabVIEW software and the description of the experimental test, which was carried out during heating season, ensuring normal building operation during the entire monitoring period. Finally, this dissertation presents the study developed in simulation environment to investigate the potential of the control logic for the evaluation of retrofitting scenarios. The focus is on the definition of the main MPC simulator components and on the results obtained by testing one of the intervention scenarios

Optimizing Energy Baseline for Medium Size Office Using Hybrid EnergyPlus-Evolutionary Programming (EP)

This paper presents an optimization approach of developing building energy baseline for medium sized office using Evolutionary Programming (EP) in comparison with direct methods. This paper applies simulation-based approach by coupling Matlab and EnergyPlus to perform energy building simulation and obtain the best energy baseline configuration with minimal error. On the other hand, direct method relies on try-and-error manually key-in methods using OpenStudio EnergyPlus simulation software. The proposed method is applied to a single story Green Energy Research Centre (GERC) office building located in UiTM Shah Alam with characteristic of partially air-conditioned buildings. The office consists of 5 different size rooms with different purposes. In this regard, 3 building parameters are taken as a decision variables including occupancies, lightings and electrical equipment. The EP objective function was set to minimize the difference between simulated and monitored energy consumption. To evaluate accuracy of building energy model, hourly criteria for Normalized Mean Biased Error (NMBE) and Coefficient of Variance Root Mean Squared Error (CV(RMSE)) endorsed by IPMVP were used. It is found that simulation-based approach has lower value of NMBE at 2.775% and CV(RMSE) at 10.949% compared to direct methods where NMBE at 79.964% while CV(RMSE) at 104.848%