Projects / AI Innovation Challenge

Increasing Clean Energy Access in Africa Through Predictive Modeling

Project completed! Results attached!

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NeedEnergy is an energy-tech startup to provide sustainable and clean energy solutions. In this two-month Omdena Challenge50 technology changemakers collaborated to develop predictive models for designing solar rooftop installations and gas pay-as-you-go reticulation services.

The problem

Sub-Saharan Africa has over 600 million people without access to electricity and electricity demand grows at an annual growth rate of 11%, the highest rate of any region worldwide. The number grows to over 700 million if clean cooking energy sources are considered as most people still rely on firewood and charcoal for their day-to-day cooking. These are just a few of the many additional challenges: 

  • The Grid is getting old and results in increased maintenance and operation cost.
  • Cost for unplanned maintenance and unforeseen faults is a pain for utilities and results in loss of revenue.
  • The Grid has not fully migrated to the edge or cloud to benefit from industry 4.0.
  • Data is in abundance but most of it is not utilized, a potential to start solving the above mentioned.

Electricity demand for commercial spaces will grow to 390 TWh by 2040 and 70% of this demand will be covered by renewable solar PV energy. This sector will experience one of the biggest energy transitions and an opportunity for a more m modern architecture for the grid of the future.

The project outcomes

NeedEnergy intends to use predictive analytics for designing solar solutions or clean energy solutions for clients based on their projected energy usage/profile. This will help to increase energy adoption where it is most needed.

You will help to accomplish this by leveraging NeedEnergy`s network of smart energy monitors for both electricity and gas. This will help with decision-making for Commercial and Industrial (C&I) clients who are transitioning to renewable energy. The analytics insights will also be used for energy suppliers. For example, gas suppliers can better plan deliveries and inventory based on the data.

In this project, you will also build predictive models to detect anomalies in the operation of the installed solar asset. An integration with IBM Deep Thunder will be ideal so that weather influences on the installation can be put into perspective when designing or operating the solar installation. 

The data

For the project, the data is classified into two main buckets, which we will use to varying degrees depending on how the project unfolds:

  • Historical Data (realized data) – This information contains the highest signal-to-noise ratio and high relevance but is expensive to collect both financially and timely.
    • User data obtained from smart meters onsight.
    • Demographic information obtained from public entities like the local utility and Regional Power Trading Data (research paper to be shared)
  • Forward-Looking Information – This information is used to provide a broader context for prediction purposes and improve accuracy when dealing with new/unseen situations. It takes into account things that may not appear in historical data sets.
    • Weather information
    • News
    • Trading Prices

The Omdena team built internal databases to store this information (relational and time series) and also develop an API to allow for easy access in production and for research purposes.

Streamlit interactive dashboard showing short-term and long-term energy demand - Source: Omdena

Streamlit interactive dashboard showing short-term and long-term energy demand – Source: Omdena

You can view and explore the dashboard using this link. To read more about how the data was collected till how that dashboard was built, please check the articles below.

Need Energy about the AI Challenge results

Your benefits

Join a thriving AI community in 88 countries

Connect with changemakers from around the world

Adress a real-world problem with your skills

Apply your skill-set while setting the stage for a meaningful career


Good English

A good/very good grasp in computer science and/or mathematics

Student, (aspiring) data scientist, (senior) ML engineer, data engineer, or domain expert (no need for AI expertise)

Programming experience with C/C++, C#, Java, Python, Javascript or similar

Understanding of ML and Deep learning algorithms

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