Machine learning algorithms can accurately predict the amount of electricity generated by stochastic energy sources such as wind turbines and solar panels, effectively  helping optimization algorithms to balance the grid helping the transition to renewable energy without destabilizing the power grid.Machine learning models are able to generate very accurate energy demand and consumption forecasts. Those predictions are used by facilities and building managers, energy companies and utilities companies to deploy energy-saving policies.

At LotusLabs using machine learning we can predict demand for energy consumption and make optimized pricing recommendations to help the energy companies meet target goals. Implementing machine learning techniques coupled with optimization software we are able to learn non-linear correlations between energy supply and demand to make optimal pricing recommendation.

Countries are gradually moving towards establishing a ‘smart grid’, a “fully automated power delivery network that monitors and controls every consumer and node, ensuring a two-way flow of electricity and information”. Energy departments in many countries are installing millions of smart meters, devices on the consumer end that monitor energy demand and supply. The idea is to connect smart energy units with the grid and optimize electricity flow during off-peak times, lowering prices for the customer while also relaxing the workload of the grid. At LotusLabs we are smart grid experts and can deliver tailored solutions for your business.

Detecting when Electric Vehicles are plugged into the grid allows utilities to suggest time-of-use rates and encourage overnight charging. Moreover, with our algorithms it is easier to profile the EV brand based on its load curve. Electric cars can also be used as a battery for smart grids.

AI Solutions help in the analysis of power generation and power outages and help solve problems by analysing historical data and forecasting outages, and malfunctions in the power grid.Our algorithms can constantly monitor, analyze energy consumption, detect emerging problems and assist in the analysis to improve performance.

By feeding customer characteristics, behavior, and billing information into a machine learning tool utilities can cut churn and increase their customers’ experience and satisfaction. Machine learning can help utilities helping their customers to identify homes at greatest risk of switching providers, listen to their concerns, and offer solutions.

Machine learning models could ultimately raise program participation rates as much as 20 percent.

Utilities can improve customer satisfaction by providing personalized insights and analysis, breaking down homes’ energy consumption by appliance. This can help their customers understand their energy behavior and pinpoint the biggest saving opportunities. LotusLabs algorithm takes into account meter reads, weather and households characteristics to create energy efficiency advice and disaggregated profile.