Electricity demand forecasting for decentralised energy management

The world is experiencing a fourth industrial revolution.Rapid development of technologies is advancing smart infrastructure opportunities.Experts observe decarbonisation,digitalisation and decentralisation as the main drivers for change.In electrical power systems a downturn of centralised conventional fossil fuel fired power plants and increased proportion of distributed power generation adds to the already troublesome outlook for op-erators of low-inertia energy systems.In the absence of reliable real-time demand forecasting measures,effective decentralised demand-side energy planning is often problematic.In this work we formulate a simple yet highly effective lumped model for forecasting the rate at which electricity is consumed.The methodology presented focuses on the potential adoption by a regional electricity network operator with inadequate real-time energy data who requires knowledge of the wider aggregated future rate of energy consumption.Thus,contributing to a reduction in the demand of state-owned generation power plants.The forecasting session is constructed initially through analysis of a chronological sequence of discrete observations.Historical demand data shows behaviour that allows the use of dimensionality reduction techniques.Combined with piecewise interpolation an electricity demand forecasting methodology is formulated.Solutions of short-term forecasting problems provide credible predictions for energy demand.Calculations for medium-term forecasts that extend beyond 6-months are also very promising.The forecasting method provides a way to advance a novel decentralised informatics,optimisa-tion and control framework for small island power systems or distributed grid-edge systems as part of an evolving demand response service.