3 research outputs found
Towards Sustainable Deep Learning for Multi-Label Classification on NILM
Non-intrusive load monitoring (NILM) is the process of obtaining
appliance-level data from a single metering point, measuring total electricity
consumption of a household or a business. Appliance-level data can be directly
used for demand response applications and energy management systems as well as
for awareness raising and motivation for improvements in energy efficiency and
reduction in the carbon footprint. Recently, classical machine learning and
deep learning (DL) techniques became very popular and proved as highly
effective for NILM classification, but with the growing complexity these
methods are faced with significant computational and energy demands during both
their training and operation. In this paper, we introduce a novel DL model
aimed at enhanced multi-label classification of NILM with improved computation
and energy efficiency. We also propose a testing methodology for comparison of
different models using data synthesized from the measurement datasets so as to
better represent real-world scenarios. Compared to the state-of-the-art, the
proposed model has its carbon footprint reduced by more than 23% while
providing on average approximately 8 percentage points in performance
improvement when testing on data derived from REFIT and UK-DALE datasets
Sustainable Deep Learning for Wireless Fingerprinting Localization
Trenutno že precej popularne in razširjene storitve na podlagi lokacije neizogibno postajajo del prihajajočih mobilnih storitev, poslovnih procesov in infrastrukture, ki te storitve podpira. Vedno bolj razširjeno in popularno strojno učenje z uporabo globokih nevronskih mrež daje odlične rezultate za brezžično lokaliziranje z radiofrekvenčnimi odtisi na podlagi obsežnih podatkov o radijskih meritvah v zaprtih prostorih.
Vendar z naraščajočo kompleksnostjo modelov njihovo učenje in uporaba postajata računsko zelo intenzivna in energetsko potratna. Če upoštevamo samo mobilne uporabnike, ki jih bo po ocenah do konca leta 2025 več kot 7,48 milijarde, in če predpostavimo, da bo potrebno v povprečju opraviti samo eno lokalizacijo na uporabnika na uro, bi morali modeli strojnega učenja, uporabljeni za izračun lokacije, opraviti 65 * 10^12 napovedi na leto. Če oceni dodamo še deset milijard drugih povezanih naprav in aplikacij, ki so močno odvisne od pogostejših posodobitev lokacije, postane jasno, da bo lokalizacija znatno prispevala k emisijam ogljikovega dioksida in ogljičnemu odtisu tehnologije, če ne bodo razviti in uporabljeni energetsko učinkovitejši modeli.
Ta ugotovitev nas je spodbudila k delu na optimizaciji kompleksnosti modela za lokalizacijo v zaprtih prostorih. Razviti model temelji na globokem učenju, in je energetsko učinkovitejši v primerjavi s sorodnimi najsodobnejšimi pristopi, hkrati pa kaže le neznatno poslabšanje zmogljivosti. Podrobna ocena delovanja kaže, da predlagani model ustvari le 58,% emisij ogljikovega dioksida, hkrati pa ohranja 98,7% celotne zmogljivosti v primerjavi z najsodobnejšim primerljivim modelom drugih raziskovalcev. Poleg modela smo razvili metodologijo za izračun kompleksnosti modela globokega učenja in s tem povezanega ogljičnega odtisa za njegovo učenje in uporabo.Location based services have gained significant popularity among end users and are now inevitably becoming part of new wireless infrastructures and emerging business processes. Deep learning artificial intelligence methods have proved as highly effective for wireless fingerprinting localization based on extensive indoor radio measurement data.
Nevertheless, as these methods grow in complexity, their computational requirements and consequently energy consumption become significantly burdensome, both during the training phase and in subsequent operations. This concern is further exacerbated when considering the number of mobile users is estimated to exceed 7.48 billion by the end of 2025. Assuming that the networks serving these users will need to conduct only one localization per user per hour on average, the machine learning models employed for these calculations would be compelled to execute 65 * 10^12 predictions per year. Furthermore, when factoring in the tens of billions of other interconnected devices and applications that heavily rely on more frequent location updates, it becomes evident that localization will make a substantial contribution to carbon emissions unless more energy-efficient models are developed and adopted.
This motivated our work on a new deep learning architecture for indoor localization that achieves superior energy efficiency compared to existing state-of-the-art methods, with minimal performance degradation. To comprehensively evaluate the performance of our proposed model, we conducted a detailed analysis, revealing that it generates a only 58% of the carbon footprint while maintaining 98.7% of the overall performance compared to state of the art models external to our group. Additionally, we elaborate on a methodology for assessing the complexity of the DL model, enabling the estimation of its CO2 footprint during both training and operational phases