Thesis 1: Don’t ask people what they do or want. Get data instead.
What sounds counterintuitive and even arrogant is based on much of the experience we gathered at Mobisol over almost 10 years, when it comes to understanding customers’ needs and behavior.
When I started Mobisol we had not a clue about what the customers really wanted in terms of energy access. We did not understand demand or willingness to pay. The famous quote (probably) by Henry Ford „If I had asked people what they wanted, they would have said faster horses.” reminds us relying only on customers’ feedback may not be the best input for product development.
Before we started deploying our systems we went to the field with questionnaires and the hope to find out what people’s energy needs really were.
The diversity in replies, even within members of the same family about their energy usage, underlined that the results of our questionnaires should be understood and evaluated with a pinch of salt, or maybe better two or three.
Thesis 2: Objective data gathering is the key to precise product development.
Our understanding of electricity demand started improving when we were able to gather „objective“ data generated by sensors.
To get an understanding of what the customer behavior really was, we embedded a data modem to enable our solar systems to „talk“ to us remotely, no matter how far away in rural areas they were installed.
This remote monitoring was on the innovative edge back then, today it is nearly the norm. So we were extremely excited when we had the first 100 solar systems in the field sending live data to our database. We could see, when people switched on their light or TV. We were able to differentiate the different appliances and got a good digital (human-proof) pattern of usage time and power consumption. This data helped us to actually understand our customers, their needs and the patterns of energy usage. In return we were able to adopt the Mobisol product offering, optimizing the size of panel and battery, making it a more affordable and satisfying solution to the customer base.
Thesis 3: Making usage data a public resource will unlock solar powered productive use appliances.
This experience back in the days inspired the data part of our activities at the A2EI today. The first decision was to create a public data and usage pattern library managed by us and put at the disposition of the sector.
Only if we stop guessing and start calibrating both solar systems and productive use appliances will meet the needs of the users.
We have started the data project by recording usage data of generators in Nigeria as well as their impact on air quality and noise levels. Such data – which is paramount to correctly dimension solar systems able to substitute fuel generators – was until now not available. User questionnaires turned out to have too high a variance – such that remote monitored data was the preferred solution.
Data gathering as the main source of feedback on the usage of appliances can also be applied to other fields, such as for example cooking. We have implemented sensors to monitor the usage of electric cooking solutions in Tanzania, which until then had been observed and described by people, which again induced human errors.
We are expanding the data sensor development to yet other fields, such as agriculture. Currently in our pipeline are sensors to measure water table levels and solar water pump usage so we can evaluate the ecological impact of solar water pumping. We are also planning for sensory boards that will monitor the performance and usage of agricultural machinery such as grinding mills and oil presses and adjust the power input in order to run the equipment at peak efficiency.
The first data sets are being uploaded to the library as we speak and it will grow fast over time as new CSV files fill the archive. The library will develop to be the go-to place to look for reliable and unique usage data which will allow product engineers to develop their systems and machines to the real needs of the people.
You can fill in a data request at www.a2ei.org/data in order to be granted access to the library. An executive summary on the data findings is available as PDF at www.a2ei.org/news/stopguessing
Applied example 1:
In Nigeria the A2EI and its partner organization Creeds Energy have installed more than 160 smart meters to monitor grid availability and generator usage. Surely one is not surprised to see a correlation between grid down time and generator usage, and it is interesting to understand how often the grid is down. But what is even more interesting is to understand how much load is powered by the generators. This was a flabbergasting discovery and made us realize how powerful the data we collected is. On average, only 18% of the generator capacity is used and well above 80% of the generators are running below a peak power of 600W. To find out that most 3000W generators which we are monitoring are hovering at 600W or below, was a total eye opener, a little bit like 2011 when we received the first data from Mobisol’s pilot systems which indicated that solar power can very well serve off-grid households and businesses.
We are now 5 months into the first data collecting exercise of the A2EI, but a preliminary glimpse into the results allows us to believe that a rightly dimensioned solar (business as opposed to home) system will on one hand satisfy the power needs of the people and on the other hand be an affordable alternative to the current status quo of dirty and polluting petrol guzzling generators.
Applied example 2:
In Tanzania we looked into the usage of e-cooking solutions to replace charcoal cookstoves.
While initial opinions and reactions of potential users was: „With that thing it would be impossible to cook Ugali (the maize staple food of Tanzania)“, we and them were actually happy to find out that e-cooked Ugali was even easier to make than the stovetop recipe. Even better and because we had sensors installed, we learned that customers continued to use the e-stoves longafter our team had left.
The outcome shows there is an appetite from our test users not only for Ugali but to leave their traditional cooking habits behind and to move towards real clean cooking. Now with this first set of results, we have to investigate how such a cooking solution needs to be designed to make it affordable. We are currently collecting data on the performance of pressure cookers under different operating conditions and using that data to understand how the equipment can be designed efficiently. We are also planning to gather more data on the energy expenditure of rural households with a partner organization, which will allow us to determine the true cost of electric cooking.