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.
It’s critical for many companies to accurately forecast electricity load. Electricity load is a fundamental input to operations of transmission system operators (TSOs) and is important for industrial producers to balance their decisions on electricity procurement.
Owners of photovoltaic (PV) plants, electricity traders, and system regulators need accurate forecasts of production of the PV plants, for different time horizons and different granularities, to optimize their maintenance, trading, and regulation strategies; the same goes for wind production. These examples only scratch the surface of the widespread presence of time-series data in utilities.
Modeling time-series data 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.
Based on: Predictive Analytics for Time Series with InstantML, by L. Miller and Tangent Works (2021).