Sökning: "Non-intrusive load monitoring"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Non-intrusive load monitoring.

1. 1. Non-Intrusive Load Monitoring to Assess Retrofitting Work

   Master-uppsats, KTH/Matematisk statistik

   Författare :Julien Zucchet; [2022]
   Nyckelord :NILM; retrofitting work assessment; hierarchical Bayesian mixture model; NILM; hierarkisk Bayesiansk blandningsmodell; utvärdering av renoveringsarbetens effektivitet;

   Sammanfattning : Non-intrusive load monitoring (NILM) refers to a set of statistical methods for inferring information about a household from its electricity load curve, without adding any additional sensor. The aim of this master thesis is to adapt NILM techniques for the assessment of the efficiency of retrofitting work to provide a first version of a retrofitting assessment tool. LÄS MER

3. 2. Deep Neural Networks Based Disaggregation of Swedish Household Energy Consumption

   Master-uppsats,

   Författare :Praneeth Varma Bhupathiraju; [2020]
   Nyckelord :Deep learning; Non-intrusive load monitoring; disaggregation.;

   Sammanfattning : Context: In recent years, households have been increasing energy consumption to very high levels, where it is no longer sustainable. There has been a dire need to find a way to use energy more sustainably due to the increase in the usage of energy consumption. LÄS MER

4. 3. Load Identification from Aggregated Data using Generative Modeling

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Tanay Rastogi; [2019]
   Nyckelord :;

   Sammanfattning : In the view of an exponential increase in demand for energy, there is a need to come up with a sustainable energy consumption system in residential buildings. Several pieces of research show that this can be achieved by providing real-time energy consumption feedback of each appliance to its residents. LÄS MER

5. 4. An approach to evaluate machine learning algorithms for appliance classification

   Kandidat-uppsats, Malmö universitet/Fakulteten för teknik och samhälle (TS)

   Författare :Charlie Olsson; David Hurtig; [2019]
   Nyckelord :Machinelearning; lstm; NILM; evaluate; algorithms; appliance; classification; machine; learning;

   Sammanfattning : A cheap and powerful solution to lower the electricity usage and making the residents more energy aware in a home is to simply make the residents aware of what appliances that are consuming electricity. Meaning the residents can then take decisions to turn them off in order to save energy. LÄS MER

7. 5. How do users understand and act upon disaggregated feedback in Smappee?

   Master-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)

   Författare :Erik Rosberg; [2016]
   Nyckelord :disaggregated feedback; smart meters; Smappee;

   Sammanfattning : Giving feedback to households about their energy consumption has been seen by many as a necessity in order for households to reduce their energy consumption and lower their carbon footprint. Many studies have been made on how smart meters, that give feedback on the total consumption, are used and their effect on the consumption. LÄS MER