Energy Consumption, Active Power Loss Forecasting, Heat Consumption, Building Energy Optimization
From electricity generation to storage, transport, distribution, and consumption, the energy industry value chain is extremely volatile and is being rapidly transformed by global market trends such as deregulation, renewable energy, carbon footprint reduction, energy exchanges and smart meter/grid technologies. Modeling for the energy industry supports key decision making that can affect short-term supply and demand planning, energy efficiency, spot market futures, energy production, and long-term capacity management.
Ai-MicroCloud® enables you to develop, deploy and manage bespoke real-time domain-specific Ai applications in energy optimization, energy consumption and active power loss forecasting.
Accurate energy forecasting is a crucial factor underlying utility company financial performance. They need accurate energy forecasts since extreme wholesale price volatility requires hedging against volume and price risk. It is important to determine which input variable have the highest relevance in calculating forecast. Examples of explanatory data variables can be historical load data in different levels of aggregation, as well as real-time measurement, weather data, calendar information, day/night etc.
Electricity distribution is impacted by line resistance, outside temperature and switching states in the grid, potentially resulting in energy grid losses. Transmission system operators (TSO) must compensate for losses and manage them as it influences balance on the grid. Examples of explanatory variables include historical actual values for losses and technical information such as relevant points on the power grid, load and weather data.
Domestic heat consumption for water heating (cooking, bathing) and space heating. Continuous monitoring and detection of anomalous values can indicate issues such as ruptured pipes, loss of system pressure, water diversion or issues with radiators or boilers.
Buildings produce, store, and consume power, interacting with multiple suppliers – Stable (nuclear, hydro), controlled variables (coal, gas) and variable (Solar, wind). Building managers can harness AI to enable decision-making on power optimization alternatives, including drawing power, producing and uploading energy; producing and consuming energy; and producing and storing energy.
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