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project:hotornot [2016/04/10 15:28] – [Links] erwinsaegesser | project:hotornot [2016/04/10 21:48] – [Team] derrickoswald | ||
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===== 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 or her roof? | + | > **Want to know who's hot** for putting a solar power system of his or her roof? |
- | **Why spend unnecessary | + | > **Why spend unnecessary |
+ | |||
+ | > We know **your next solar customer**! | ||
+ | |||
+ | |||
+ | ---- | ||
- | We know **your next solar customer**! | ||
===Mission=== | ===Mission=== | ||
- | Our mission is to help increase the penetration of renewable energy production. | + | **Our mission is to help increase the penetration of renewable energy production** |
===Value proposition=== | ===Value proposition=== | ||
- | 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 (quizz), injected in popular websites such as "20 Minuten" | + | > 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" |
- | **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**. | + | >> |
- | **Further simplified** | + | >>> |
===Main User-Story=== | ===Main User-Story=== | ||
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===Further potential customers are=== | ===Further potential customers are=== | ||
- | * Public institutions such as Cantons and Municipalities | + | * Public institutions such as Cantons and Municipalities |
- | * Planning and engineering companies / product developers | + | * Planning and engineering companies / product developers |
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=== Data === | === Data === | ||
- | BEN Energy (https:// | + | BEN Energy (https:// |
For detailed information see here: [[project: | For detailed information see here: [[project: | ||
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=== Goal === | === Goal === | ||
- | The main goal of our project was to test whether social demographic and economic information is a value add in order to determine | + | The main goal of our project was to test whether social demographic and economic information is of value in determining |
=== Method === | === 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 team members put together a list of properties they thought would influence the results. After a first iteration, | + | 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 team members put together a list of properties they thought would influence the results. After a first iteration, |
Finally the team concluded the project by creating a website to showcase the machine learning result as well as the business view. | Finally the team concluded the project by creating a website to showcase the machine learning result as well as the business view. | ||
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=== Results / Findings === | === Results / Findings === | ||
- | The team was able to derive a specific list of customers with an affinity for solar. Based on the results | + | The team was able to derive a specific list of customers with an affinity for solar. Based on the results the conclusion |
===== Team ===== | ===== Team ===== | ||
- | * [[user: | + | * Paul Affentranger / affentranger@afca.ch |
- | * [[user: | + | * Christian Eggenberger / eggenberger@afca.ch |
- | * @Team - pls add your usernames | + | * Moritz Kulawik / moritz.kulawik@lu.ch / [[user: |
- | + | * Claire-Michelle Loock / claire-michelle.loock@ben-energy.com | |
- | === Teamwork | + | * Urs Mändl |
- | + | * Derrick Oswald / derrick.oswald@9code.ch / [[user:derrickoswald]] | |
- | {{: | + | * Erwin Sägesser / erwin.saegesser@nis.ch / [[user: |
- | Teamwork 1 | + | * Nadim Schumann / nadim.schumann@qbis.ch |
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- | {{: | + | |
- | Teamwork 2 | + | |
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- | {{: | + | |
- | Teamwork 3 | + | |
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- | {{: | + | |
- | Business model | + | |
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- | {{:project: | + | |
- | Machine learning sketch | + | |
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- | {{: | + | |
- | UX Design 1 | + | |
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- | {{: | + | |
- | UX Design 2 | + | |
===== Links ===== | ===== Links ===== | ||
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* [[project: | * [[project: | ||
* [[project: | * [[project: | ||
- | * [[project:motornot:Azure machine learning]] | + | * [[project:hotornot:Azure machine learning]] |
+ | * [[project: | ||
{{tag> | {{tag> |