FORECASTING Energy Consumption and Generation - IntraDay and DayAhead / Artificial Intelligence

Problem/Challenge - Current solutions have a significant margin of error (Delta between the forecasted value and the real one) - On average this Delta is between 20% and 40%... - This translates into uncessary addtional energy costs, either from energy supply contracts with rates higher than necessary (Delta Negative) or from penalties for exceeding the contracted value (Delta positive) - In addition, the current systems generally cannot react fast enough to external factors of changes in the consumption pattern, having to make manual adjustments ... (example COVID and changes in production/load, temporary stoppage of a part of the plant, or re-startup of a part of the plant that was on stand-by, etc.) Proposed solution - Forecasting of Consumption (and Generation), with very high accuracy (Delta between estimated and real value is reduced/optimized drastically versus conventional solutions) - Based on Artificial Intelligence Algorithms - Supports both 'DayAhead' (days, weeks, ....) and 'IntraDay' (prediction within the next few hours or fraction of an hour ...) fsight.PREDICT uses state-of-the-art machine learning and artificial intelligence technology to automatically forecast electricity consumption and generation for sinble plants/installations, or aggregated forecasting of portfolios of distributed plants Who can benefit from this solution? - Utilities - energy retailers - energy producers - network operators - Heavy Energy-Consumption Industries - Logistics or large Buildings Some benefits - Improve energy forecast accuracy by up to 40% - Significant reduction of the total to pay of the electricity bill (for Consumption points) - optimization of supply contracts (for Generation points) - Improve energy trading strategies - Reduce imbalance costs - Adhere to regulatory requirements