عنوان انگلیسی مقاله:
Electricity demand forecasting for decentralised energy management
ترجمه فارسی عنوان مقاله:
پیش بینی تقاضای برق برای مدیریت انرژی غیر متمرکز
Sciencedirect - Elsevier - Energy and Built Environment, 1 (2020) 178-186. doi:10.1016/j.enbenv.2020.01.001
Sean Williams ∗ , Michael Short
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.
Keywords: Demand response | Decentralised | Grid edge | Time series forecasting