In the energy stratosphere, utilities, energy providers, and system operators might fail to line up with periods of high or low electricity demand. Should supply be higher than demand, billions are wasted on generating and purchasing unnecessary power or on insurance costs, and penalties imposed. In the end-user reality, a ‘’black-out’’ situation might occur. This usually happens because load forecasting, a foundation of the energy planning sector, is not accurate. Although utilities and specialized load forecasting companies use data from smart meters and weather patterns, they manage to predict only a 93-95% accuracy, which results in billions being lost each year.
Demand planning and grid management
The reality is that energy consumption is extremely volatile, far more volatile than any other commodity while a common feature of electricity is that the spikes’ intensity is non-homogenous in time. What's more, electricity penetration to the grid being generated from renewables (wind, solar), adds more variability, making system reliability planning hard. Reliable, secure, and cost-optimal planning is extremely important for Distributors Systems Operations and Transmission System Operators. To better correspond to the grid’s operational capabilities, solar forecasting will be crucial in the years to come, as efforts to manage the grid when the load is at a peak would be intensified.
Customer clustering and behavioral-based load shifting
Based on data coming from load forecasting, clustering of different end-customers groups can be implemented for energy providers to identify electricity usage patterns. These insights can be utilized to add to a 360º customer view or to personalized marketing propositions like new tariffs and incentives to better engage end customers, help them save money, and reduce churn rates at the same time. Another idea is that solar forecasting might be used as a mainstay of energy storage or demand response systems.
Forecast time horizon
That being said, timing is perfect for utilities and retail energy providers to update their load forecasting practices to reflect today’s rapidly changing energy landscape. Access to smart meter data (standard AMI or even more granular) via AI and machine learning models can be the game-changer. Smart meters can provide data on various time horizons; day-ahead forecasting on the 15-60 minute data samples, long-term, weekly, monthly and yearly forecasting services on the basis of daily consumption data samples. The more sophisticated real-time data, for the next 60 minutes on the basis of 1-minute data samples, is also possible to be intraday forecasted, however, investing in hardware is a prerequisite. Based on them, real-time EV charging and solar generation prediction metrics will be made available.
What NET2GRID does
NET2GRID can provide accurate forecasting services addressing all of the above needs and challenges, being an asset to utilities and grid operators. Our 98,1% load forecasting accuracy can substantially help energy players to save millions annually in energy procurement while adding value to those eager to develop demand response or other services in individual households. NET2GRID predicts consumption not only by taking into account historical and weather data but also, smart meter data, dates, and other inputs which allow us to make a highly reliable demand prediction. Our innovation relies heavily on our algorithms capable of filling missing data and replicating future load consumption, and on our cost-efficient and country agnostic and easy-to-install hardware, capable of real-time energy data acquisition.