Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

The current approaches for energy consumption optimisation in buildings are mainly reactive or focus on scheduling of daily/weekly operation modes in heating. Machine Learning (ML)-based advanced control methods have been demonstrated to improve energy efficiency when compared to these traditional methods. However, placing of ML-based models close to the buildings is not straightforward. Firstly, edge-devices typically have lower capabilities in terms of processing power, memory, and storage, which may limit execution of ML-based inference at the edge. Secondly, associated building information should be kept private. Thirdly, network access may be limited for serving a large number of edge devices. The contribution of this paper is an architecture, which enables training of ML-based models for energy consumption prediction in private cloud domain, and transfer of the models to edge nodes for prediction in Kubernetes environment. Additionally, predictors at the edge nodes can be automatically updated without interrupting operation. Performance results with sensor-based devices (Raspberry Pi 4 and Jetson Nano) indicated that a satisfactory prediction latency (~7–9 s) can be achieved within the research context. However, model switching led to an increase in prediction latency (~9–13 s). Partial evaluation of a Reference Architecture for edge computing systems, which was used as a starting point for architecture design, may be considered as an additional contribution of the paper

Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator

Accurate short-term forecasts of building energy consumption are necessary for profitable demand response. Short-term forecasting methods can be roughly classified into physics-based modelling and data-based modelling. Both of these approaches have their advantages and disadvantages and it would be therefore ideal to combine them. This paper proposes a novel approach that allows us to combine the best parts of physics-based modelling and machine learning while avoiding many of their drawbacks. A key idea in the approach is to provide a variety of building parameters as input for an Artificial Neural Network (ANN) and train the model with data from a large group of simulated buildings. The hypothesis is that this forces the ANN model to learn the underlying simulation model-based physics, and thus enables the ANN model to be used in place of the simulator. The advantages of this type of model is the combination of robustness and accuracy from a high-detail physics-based model with the inference speed, ease of deployment, and support for gradient based optimization provided by the ANN model. To evaluate the approach, an ANN model was developed and trained with simulated data from 900–11,700 buildings, including equal distribution of office buildings, apartment buildings, and detached houses. The performance of the ANN model was evaluated with a test set consisting of 60 buildings (20 buildings for each category). The normalized root mean square errors (NRMSE) were on average 0.050, 0.026, 0.052 for apartment buildings, office buildings, and detached houses, respectively. The results show that the model was able to approximate the simulator with good accuracy also outside of the training data distribution and generalize to new buildings in new geographical locations without any building specific heat demand data

Semantic interoperability framework for smart spaces

Abstract At the heart of the smart space vision is the idea that devices interoperate with each other autonomously to assist people in their everyday activities. In order to make this vision a reality, it is important to achieve semantic-level interoperability between devices. The goal of this dissertation is to enable Semantic Web technology-based interoperability in smart spaces. There are many challenges that need to be solved before this goal can be achieved. In this dissertation, the focus has been on the following four challenges: The first challenge is that the Semantic Web technologies have neither been designed for sharing real-time data nor large packets of data such as video and audio files. This makes it challenging to apply them in smart spaces, where it is typical that devices produce and consume this type of data. The second challenge is the verbose syntax and encoding formats of Semantic Web technologies that make it difficult to utilise them in resource-constrained devices and networks. The third challenge is the heterogeneity of smart space communication technologies that makes it difficult to achieve interoperability even at the connectivity level. The fourth challenge is to provide users with simple means to interact with and configure smart spaces where device interoperability is based on Semantic Web technologies. Even though autonomous operation of devices is a core idea in smart spaces, this is still important in order to achieve successful end-user adoption. The main result of this dissertation is a semantic interoperability framework, which consists of following individual contributions: 1) a semantic-level interoperability architecture for smart spaces, 2) a knowledge sharing protocol for resource-constrained devices and networks, and 3) an approach to configuring Semantic Web-based smart spaces. The architecture, protocol and smart space configuration approach are evaluated with several reference implementations of the framework components and proof-of-concept smart spaces that are also key contributions of this dissertation.Tiivistelmä Älytilavision ydinajatuksena on, että erilaiset laitteet tuottavat yhteistyössä ihmisten elämää helpottavia palveluita. Vision toteutumisen kannalta on tärkeää saavuttaa semanttisen tason yhteentoimivuus laitteiden välillä. Tämän väitöskirjan tavoitteena on mahdollistaa semanttisen webin teknologioihin pohjautuva yhteentoimivuus älytilan laitteiden välillä. Monenlaisia haasteita täytyy ratkaista, ennen kuin tämä tavoite voidaan saavuttaa. Tässä työssä keskityttiin seuraaviin neljään haasteeseen: Ensimmäinen haaste on, että semanttisen webin teknologioita ei ole suunniteltu reaaliaikaiseen kommunikaatioon, eivätkä ne sovellu isojen tiedostojen jakamiseen. Tämän vuoksi on haasteellista hyödyntää niitä älytiloissa, joissa laitteet tyypillisesti jakavat tällaista tietoa. Toinen haaste on, että semanttisen webin teknologiat perustuvat syntakseihin ja koodausformaatteihin, jotka tuottavat laitteiden kannalta tarpeettoman pitkiä viestejä. Tämä tekee niiden hyödyntämisestä hankalaa resurssirajoittuneissa laitteissa ja verkoissa. Kolmas haaste on, että älytiloissa hyödynnetään hyvin erilaisia kommunikaatioteknologioita, minkä vuoksi jopa tiedonsiirto laitteiden välillä on haasteellista. Neljäs haaste on tarjota loppukäyttäjälle helppoja menetelmiä sekä vuorovaikutukseen semanttiseen webiin pohjautuvien älytilojen kanssa että tällaisen älytilan muokkaamiseen käyttäjän tarpeiden mukaiseksi. Vaikka laitteiden itsenäinen toiminta onkin älytilojen perusajatuksia, tämä on kuitenkin tärkeää teknologian hyväksymisen ja käyttöönoton kannalta. Väitöskirjan päätulos on laitteiden semanttisen yhteentoimivuuden viitekehys, joka koostuu seuraavista itsenäisistä kontribuutioista: 1) semanttisen tason yhteentoimivuusarkkitehtuuri älytiloille, 2) tiedonjakoprotokolla resurssirajoittuneille laitteille ja verkoille sekä 3) menetelmä semanttiseen webiin pohjautuvien älytilojen konfigurointiin. Näiden kontribuutioiden evaluointi suoritettiin erilaisten järjestelmäkomponenttien referenssitoteutuksilla ja prototyyppiälytiloilla, jotka kuuluvat myös väitöskirjan keskeisiin kontribuutioihin

Stacked Boosters Network Architecture for Short-Term Load Forecasting in Buildings

This paper presents a novel deep learning architecture for short term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into multiple cascading univariate time series. Together with sparse interactions, parameter sharing and equivariant representations, this approach makes it possible to combat against overfitting while still achieving good presentation power with a deep network architecture. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland. The proposed architecture outperforms state-of-the-art load forecasting model in all the tasks.Comment: 16 pages, 15 figure