Electric meter

+ More real-time data and better connectivity will bring benefits for cities and their residents.

The power of real-time data is nothing new. Since ancient times having more information has provided a competitive advantage in situations ranging from the battlefield to the stock market. 

The future won’t be any different. Real-time data can help cities become more attractive while giving citizens the services they require. Real-time data and connectivity has the potential to be translated into benefits for cities and their citizens, mainly in terms of savings and optimising the usage of a city’s scarce resources.

Real-time (or even close to real-time) data will, if managed appropriately, radically improve accuracy when trying to predict the immediate future or to produce short-term forecasts. This could make usage of scarce resources, like electricity, more efficient. 

At the moment electricity can’t be stored in large quantities, at least in a competitive way. At the same time there is an increasing amount of intermittent power generated by renewables adding a bigger challenge to the balance of the system. Therefore it’s critical to have the right amount in the grid at any moment.

In the UK, the National Grid publishes the country consumption figures in real-time and relies on historical consumption data plus a forecasting algorithm to predict future consumption. Having real-time (or close to real-time) consumption data would allow the algorithm to be continuously revised with new data – significantly improving the accuracy of forecasts, and ultimately achieving a more efficient balancing. 

It will also be possible to learn about historical changes in methodology used to construct the consumption data. In the past we could never have anticipated this information and by the time we may have come across it, it probably wouldn’t have extra benefits. 

Real-time data could be especially useful when predictions require input from another series of forecasted data (such as weather forecast data). By applying time series analysis, (for example, an autoregressive integrated moving average or ARIMA model, the most general class of models for forecasting a time series) it would be possible to identify patterns and systematic errors that, when incorporated into the algorithm, could enhance significantly the accuracy of our forecasts. 

In general, it’s fair to say that the scarcer the resource, the bigger the savings. For example in Great Britain, Elexon applies ‘cash-out’ or ‘energy imbalance’ prices to settle the difference between contracted generation or consumption and the amount that was actually generated or consumed in each half hour trading period. These cash out prices are designed to penalise companies for any imbalance. A small percentage saved in the spot market for energy would be translated into a huge amount of money. 

In a market like that for electricity, where to encourage efficient balancing the ’cash out’  prices carry big premiums, the benefits that real time data could bring should not be ignored.

Of course, energy companies would be the main beneficiaries. But with the right regulation in place, savings should be passed on to consumers. 

Moreover, real-time data combined with technology such as smart grids and smart meters will empower consumers, facilitating switching and bringing estimated billing to an end. They will also enhance demand response, helping consumers react faster to electricity prices while facilitating troubleshooting.  

However smart technology needs to be complemented with energy-saving awareness in order to bring significant savings. A survey carried out by British Gas found that changes in behaviour introduced by smart meters have led to financial savings of 54% off consumer’s energy bills, with 64% of those identifying savings of up to £75 per year.

Energy security of supply would also improve by citizens shifting their power consumption to off-peak times. This will help to deal with tight electricity margins, currently an issue in the UK due to the closure of older power stations sooner than had previously been expected.

This is why I believe data, if well managed and with the right protection, is key to efficiency, the basic element for cities to face the challenges of continuing growth.