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

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

Prototyping the new-guard portable device for radiation detection

A novel and efficient radiation detection algorithm fused together with a new generation detection unit will produce an effective detector to battle field radiation measurement problems and reduce field surveyor work hazards. The SPRT (Sequential Probability Ratio Test) algorithm helps increase reliability and speed of radiation detection; the new generation detector improves the detector\u27s flexibilities, applications, and functions. A prototype system of the New Generation User Adaptable Radiation Detector (New-GUARD) is developed and analyzed to determine the system feasibility of usage, development, and safety for radiation detection. Development stages include the implementation of the Graphical User Interface (GUI), building the ideal hardware components for the detection unit, and the integration of the two to form the complete New-GUARD system. The New-GUARD GUI is created using the Visual Basic .NET programming language along with the .NET Compact Framework and Windows Mobile 6 SDK for all Windows Mobile based devices. The hardware portion is implemented using a microcontroller that sends data out to the GUI via a wireless transmission medium. New-GUARD system performance metrics are provided to show real-time processing capabilities. Lastly, alternative New-GUARD hardware designs as well as a future plan to eliminate human presence entirely from radiation fields are discussed

Local conditions for the decentralization of energy systems

Local energy systems (LES) are designed to decarbonize, balance, and coordinate supply, storage and demand resources. Which local conditions enable LES to flourish? Using a unique dataset of 146 LES projects in the UK from 2010 to 2020, we apply econometric methods to identify energy, institutional and socio-economic conditions significantly associated with LES, but not other local energy forms. We show distributed power generation, low-carbon infrastructure firm activity, local government strategy and active energy efficiency markets are enablers of LES involving multiple actors, sectors and skill sets. These conditions describe a clear policy agenda for stimulating and supporting emerging local energy markets

Local conditions for the decentralization of energy systems

Local energy systems (LES) are designed to decarbonise, balance, and coordinate supply, storage, and demand resources. Which local conditions enable LES to flourish? Using a unique dataset of 146 LES projects in the UK from 2010-2020, we apply econometric methods to identify energy, institutional and socioeconomic conditions significantly associated with LES, but not other local energy forms. We show distributed power generation, low-carbon infrastructure firm activity, local government strategy, and active energy efficiency markets are enablers of LES involving multiple actors, sectors, and skill sets. These conditions describe a clear policy agenda for stimulating and supporting emerging local energy markets

Localization Of Sensors In Presence Of Fading And Mobility

The objective of this dissertation is to estimate the location of a sensor through analysis of signal strengths of messages received from a collection of mobile anchors. In particular, a sensor node determines its location from distance measurements to mobile anchors of known locations. We take into account the uncertainty and fluctuation of the RSS as a result of fading and take into account the decay of the RSS which is proportional to the transmitter-receiver distance power raised to the PLE. The objective is to characterize the channel in order to derive accurate distance estimates from RSS measurements and then utilize the distance estimates in locating the sensors. To characterize the channel, two techniques are presented for the mobile anchors to periodically estimate the channel\u27s PLE and fading parameter. Both techniques estimate the PLE by solving an equation via successive approximations. The formula in the first is stated directly from MLE analysis whereas in the second is derived from a simple probability analysis. Then two distance estimates are proposed, one based on a derived formula and the other based on the MLE analysis. Then a location technique is proposed where two anchors are sufficient to uniquely locate a sensor. That is, the sensor narrows down its possible locations to two when collects RSS measurements transmitted by a mobile anchor, then uniquely determines its location when given a distance to the second anchor. Analysis shows the PLE has no effect on the accuracy of the channel characterization, the normalized error in the distance estimation is invariant to the estimated distance, and accurate location estimates can be achieved from a moderate sample of RSS measurements