{"id":590,"date":"2025-12-07T16:57:08","date_gmt":"2025-12-07T16:57:08","guid":{"rendered":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/?page_id=590"},"modified":"2025-12-07T17:42:18","modified_gmt":"2025-12-07T17:42:18","slug":"intelligent-energy-consumption-fairness-in-smart-cities","status":"publish","type":"page","link":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/intelligent-energy-consumption-fairness-in-smart-cities\/","title":{"rendered":"Intelligent Energy Consumption & Fairness in Smart Cities"},"content":{"rendered":"\n

Students develop a mini-project implemented through four 1-hour challenges.
The project focuses on intelligent energy consumption and fairness-aware analytics
for smart cities, combining data cleaning, clustering, fairness evaluation, SQL-based
sampling, and machine learning modelling.<\/p>\n\n\n\n

The four challenges are designed to be done sequentially and build on each other:<\/p>\n\n\n\n

1. Challenge 1<\/a> \u2013 Data Cleaning & Outlier Detection for Smart-City Energy
2. Challenge 2<\/a> – DEI aware and fairness in data preparation (introduction)
2. Challenge 2<\/a> \u2013 Data Quality, Fairness & SQL Sampling with AIF360 (application)
3. Challenge 3<\/a> \u2013 Modelling Smart-City Energy Consumption & Fairness of Errors<\/p>\n\n\n\n

At the end of the project, you will produce a final infographic<\/em><\/strong> that summarises
your workflow, results, and main insights. The description of its content is here<\/a>.<\/p>\n\n\n\n

1. Project Learning Goals<\/h2>\n\n\n\n

By completing this project, students should be able to:<\/p>\n\n\n\n

    \n
  • Work with a real-world open dataset on smart-city energy consumption.<\/li>\n\n\n\n
  • Apply data-quality checks and statistical data cleaning techniques.<\/li>\n\n\n\n
  • Use k-means clustering to identify atypical days in energy consumption.<\/li>\n\n\n\n
  • Use the IBM AI Fairness 360 (AIF360) library to measure bias in a socio-economic dataset.<\/li>\n\n\n\n
  • Implement fairness-aware sampling strategies using SQL.<\/li>\n\n\n\n
  • Build and evaluate machine learning models for daily energy consumption prediction.<\/li>\n\n\n\n
  • Analyse the fairness of prediction errors across building groups.<\/li>\n\n\n\n
  • Communicate results and insights through a concise, visually engaging infographic.<\/li>\n<\/ul>\n\n\n\n

    2. Datasets Used<\/h2>\n\n\n\n

    2.1 Smart-City Energy Data (Open Power System Data \u2013 Household Data)<\/em><\/p>\n\n\n\n

      \n
    • Source: Open Power System Data (OPSD) \u2013 Household Data package (60-minute resolution).<\/li>\n\n\n\n
    • Example link: https:\/\/data.open-power-system-data.org\/household_data\/2020-04-15\/<\/li>\n\n\n\n
    • Main file used: household_data_60min_singleindex.csv (hourly smart-meter readings).<\/li>\n\n\n\n
    • Role in project:<\/li>\n\n\n\n
    • Used in Challenge 1 to build a cleaned daily energy dataset.<\/li>\n\n\n\n
    • The resulting aggregated dataset (energy_daily_features.csv) is reused in Challenge 3.<\/li>\n<\/ul>\n\n\n\n

      2.2 Socio-Economic & Fairness Data (Adult Census Income Dataset)<\/em><\/p>\n\n\n\n

        \n
      • Source: UCI Machine Learning Repository \u2013 Adult dataset.<\/li>\n\n\n\n
      • Accessed via: IBM AI Fairness 360 (AIF360) library (AdultDataset class).<\/li>\n\n\n\n
      • Role in project:\n
          \n
        • Used in Challenge 2 to measure bias and experiment with fairness-aware methods.<\/li>\n\n\n\n
        • Provides the classification-based fairness concepts that inspire regression fairness<\/li>\n<\/ul>\n<\/li>\n\n\n\n
        • Metrics in Challenge 3.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

          Students develop a mini-project implemented through four 1-hour challenges.The project focuses on intelligent energy consumption and fairness-aware analyticsfor smart cities, combining data cleaning, clustering, fairness evaluation, SQL-basedsampling, and machine learning modelling. The four challenges are designed to be done sequentially and build on each other: 1. Challenge 1 – Data Cleaning & Outlier Detection for […]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"class_list":["post-590","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages\/590","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/comments?post=590"}],"version-history":[{"count":6,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages\/590\/revisions"}],"predecessor-version":[{"id":609,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages\/590\/revisions\/609"}],"wp:attachment":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/media?parent=590"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}