Recent Question/Assignment

Title of dissertation project: Using smart meter energy consumption data to identify occupancy patterns in homes
Important
This is the link for smart energy consumption data that needs to analyse. BARE IN MIND ONLY THIS DATA NOT THE ONE IN internet .I’ve explained below what needs to be done
https://drive.google.com/drive/folders/1nTOPwW9fji2D-xdd1uoPzfVhMGylEMXa?usp=sharing
Project data
This project data on excel consists of four aspects of energy use within households:
1. Bedroom temperature
2. Living room temperature
3. Electricity readings
4. Gas readings
Each household was provided with smart meters to analyse values for all of the above aspects of energy use in homes. Temperature gas and electricity values were recorded every 30 minutes of everyday throughout the whole year. I have highlighted the homes that I will include in my investigation. The aim of this is to analyse in depth each household using the values provided. The dissertation I have attached was done a student of previous of this title, he only reviewed 5 households and then compared each household to each other. My aim is to include the analysis of 15 households throughout the year and conclude with an in depth comparison. In addition to this, once the dwellings in question have been analysed patterns may be obtained and behaviours may be used to determine who may be living in the households. Below is an example as to how each household is analysed. I would like you to pick 15 households e.g. EMH1 and analyse each aspect of the house such as bedroom living room temperatures, gas and electricity readings. I want you discus the patterns and behaviours of heating patterns in homes.The readings are given every 30 minutes and so you are able to build up an in depth picture of the behaviours within each household.

Introduction: By the end of 2020, about 53 million smart meters are expected to be installed in Great Britain providing half-hourly cumulative electricity and gas consumption data. Such large scale, high quality data offers new data analysis opportunities. This project will investigate the suitability of smart meter data to identify occupancy patterns in homes.
Half hourly gas consumption data has been collected from c200 dwellings located in the East Midlands for the past three years. Dwellings are divided into 24 different archetypes covering different ages ranging from 1900 to 2013 and include houses, bungalows and flats. This database will be used to investigate if occupancy patterns can be identified using whole house gas and electricity consumption data.
Aim: To investigate whether it’s possible to use the energy data to identify occupancy patterns in homes
Objectives
1. To conduct a thorough literature review related to identifying occupancy patterns in buildings using measured energy data.
2. To collect , clean and analyse the half hourly gas and electricity consumption data that has been measured in c200 dwellings
3. To discuss the suitability of the smart meter energy data for identifying occupancy patterns in homes
What to do?
Literature review
I will attach the relevant articles required for the literature review for this dissertation. The aim of the literature review is to review articles based on heating patterns in homes. The aim of the word count for the literature review is 3000 words.
Read about how other people have done this research work and identify heating patterns in homes. Also use mainly use google scholar or sciencedirect and search the keywords ' identify heating patterns in homes'.

Read different varieties articles, books or papers, then from there critical review it of previous works e.g. How they've done it, the good and bad parts, any improvements. The more you articles you do the better I grade I get (5 or 6 articles).
Methodology
- How you conducted the analysis of the data provided.
- Review and analysis of each household Minimum of 10 households overall. E.g EMH1
- Write what I have understood based on previous works and etc
- Recommendations of future works
To get good marks?
1. Review minimum 15 households for bedroom , living room , gas ,and electricity as with the screenshot above each aspect is reviewed in unison to allow a full in depth and relevant review. Talk about why the reading is different for each hours of living room, bedroom gas and electricity. IDENTIFY THE OCCUPANCY PATTERNS
2. Use Harvard referencing such as books, journals, articles etc.
3. Your dissertation will normally be between 10,000 and 15000 words in length (excluding appendices)

UK HARVARD Referencing only
sciencedirect.com
Google scholar
Links to use – from sciencedirect.com, search the keyword of my title e.g. occupancy patterns homes
Huebner, G., McMichael, M., Shipworth, D., Shipworth, M., Durand-Daubin, M. and Summerfield, A. (2013). Heating patterns in English homes: Comparing results from a national survey against common model assumptions. Building and Environment, 70, pp.298-305.
2. Guerra Santin, O. (2011). Behavioural Patterns and User Profiles related to energy consumption for heating. Energy and Buildings, 43(10), pp.2662-2672.
3. Anderson, j. (2016). Cross Ventilation in House Designs for Natural Passive Air Flow.
4. Beizaee, A., Allinson, D., Lomas, K., Foda, E. and Loveday, D. (2015). Measuring the potential of zonal space heating controls to reduce energy use in UK homes: The case of un-furbished 1930s dwellings. Energy and Buildings, 92, pp.29-44.
5. Kane, T., Firth, S., Hassan, T. and Dimitriou, V. (2017). Heating behaviour in English homes: An assessment of indirect calculation methods. Energy and Buildings, 148, pp.89-105.
6. Murray, D., Stankovic, L., Stankovic, V. and Espinoza-Orias, N. (2018). Appliance electrical consumption modelling at scale using smart meter data. Journal of Cleaner Production, 187, pp.237-249.
7.Gouveia, J., Seixas, J. and Mestre, A. (2017). Daily electricity consumption profiles from smart meters - Proxies of behavior for space heating and cooling. Energy, 141, pp.108-122.
8. Yildiz, B., Bilbao, J., Dore, J. and Sproul, A. (2017). Recent advances in the analysis of residential electricity consumption and applications of smart meter data. Applied Energy, 208, pp.402-427.
9. Marinakis, V., Doukas, H., Tsapelas, J., Mouzakitis, S., Sicilia, Á., Madrazo, L. and Sgouridis, S. (2018). From big data to smart energy services: An application for intelligent energy management. Future Generation Computer Systems.
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12. Ford, R., Pritoni, M., Sanguinetti, A. and Karlin, B. (2017). Categories and functionality of smart home technology for energy management. Building and Environment, 123, pp.543-554.
13. Goodchild, B., Ambrose, A. and Maye-Banbury, A. (2017). Storytelling as oral history: Revealing the changing experience of home heating in England. Energy Research & Social Science, 31, pp.137-144.
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And many more
Project report
Report guidelines and marking criteria