Differences
This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision Next revisionBoth sides next revision | ||
project:hotornot [2016/04/09 12:34] – [Hot or not for solar - Energy customer profiling made easy] erwinsaegesser | project:hotornot [2016/04/10 21:48] – [Team] derrickoswald | ||
---|---|---|---|
Line 1: | Line 1: | ||
===== Hot or not for solar - Energy customer profiling made easy ===== | ===== Hot or not for solar - Energy customer profiling made easy ===== | ||
- | Want to know who's hot for putting a solar power system of his roof? | + | > **Want to know who's hot** for putting a solar power system of his or her roof? |
- | Why spending | + | > **Why spend unnecessary |
- | We know your next solar customer! | + | > We know **your next solar customer**! |
- | --------------- | ||
- | Our mission is to help increasing the penetration of renewable energy production. | + | ---- |
- | --------------- | ||
- | For energy service providers and utilities who want to target specific solar power prospects | + | ===Mission=== |
- | our product "hot or not for solar" | + | **Our mission |
- | compared to traditional approaches our service takes advantage of hidden information from social demographic and economic values and uses machine learning algorithms for deriving the prospects affinity. | ||
- | In order to improve our algorithms we run surveys (quizz), injected in popular websites such as "20 Minuten" | + | ===Value proposition=== |
- | Further potential | + | > For **energy service providers and utilities** who want to target specific solar power prospects, our product **hot or not for solar** is an **energy customer profiling service** which produces a **prioritized list of customers** with an affinity for solar. Unlike traditional approaches our service takes advantage of hidden information from social demographic and economic values and uses machine learning algorithms for deriving the prospects affinity. In order to improve our algorithms we run surveys (quizzes) injected in popular websites such as "20 Minuten" |
- | Public institutions such as Cantons and Municipalities how want to tailor information/ | + | >> Simplified: For **utilities** who want to target specific solar prospects, **hot or not for solar** uses social data and machine learning to derive a **short list**. |
- | Planning and engineering companies / product developers how want to develop and sell new products | + | >>> |
- | Main user story: As an energy service provider I want to know which prospects have an affinity for installing a solar system so that I can run personalized and focused marketing/ | + | ===Main User-Story=== |
+ | As an energy service provider I want to know which prospects have an affinity for installing a solar system so that I can run personalized and focused marketing/ | ||
- | ===== Data ===== | + | ===Further potential customers are=== |
- | * List and link your actual | + | * Public institutions such as Cantons |
+ | * Planning | ||
- | ===== Team ===== | ||
- | * [[user: | + | ===== Background Information about the project ===== |
- | * [[user: | + | |
- | | + | === Data === |
+ | |||
+ | BEN Energy (https:// | ||
+ | |||
+ | For detailed information see here: [[project: | ||
+ | |||
+ | === Goal === | ||
+ | |||
+ | The main goal of our project was to test whether social demographic and economic information is of value in determining potential new solar customers. For background information about the business goals see section [[project: | ||
+ | |||
+ | === Method === | ||
+ | |||
+ | In order to test the hypothesis the team used **Microsoft Azure machine learning** and **R** (both using random forest algorithm). Furthermore the teams tested which attributes have most influence on the results. In parallel | ||
+ | |||
+ | Finally the team concluded the project by creating a website to showcase the machine learning result as well as the business view. | ||
+ | |||
+ | === Results / Findings === | ||
+ | |||
+ | The team was able to derive a specific list of customers with an affinity for solar. Based on the results the conclusion was that social demographic and economic data play a secondary role. However, with a larger, and probably more accurate, set of information the social information may become more useful. For interest groups and businesses, the team recommends doing further research. | ||
+ | ===== Team ===== | ||
+ | |||
+ | * Paul Affentranger / affentranger@afca.ch | ||
+ | * Christian Eggenberger / eggenberger@afca.ch | ||
+ | * Moritz Kulawik / moritz.kulawik@lu.ch / [[user: | ||
+ | * Claire-Michelle Loock / claire-michelle.loock@ben-energy.com | ||
+ | * Urs Mändl / urs.maendli@energie360.ch | ||
+ | * Derrick Oswald / derrick.oswald@9code.ch / [[user: | ||
+ | * Erwin Sägesser / erwin.saegesser@nis.ch / [[user: | ||
+ | * Nadim Schumann / nadim.schumann@qbis.ch | ||
===== Links ===== | ===== Links ===== | ||
- | [[project: | + | * [[project: |
+ | * [[project: | ||
+ | * [[project: | ||
+ | * [[project: | ||
+ | |||
+ | {{tag> |