Electricity Demand Forecasting by Multi-Task Learning

Demand Forecasting for Electricity

Introduction Forecasting demand is both a science and an art. Econometric methods of forecasting, in the context of energy demand forecasting, can be described as ‘the science and art of specification, estimation, testing and evaluation of models of economic processes’ that drive the demand for fuels. The need and relevance of forecasting demand for an electric utility has become a much-discuss...

Forecasting Uncertainty in Electricity Demand

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and leptokurtic caused by the nature of energy data. In this paper we propose a novel approach to estimate the time-varying conditional variance of th...

Long Term Electricity Demand Forecasting with Multi-agent-Based Model

Gated Ensemble Learning Method for Demand-Side Electricity Load Forecasting

The forecasting of building electricity demand is certain to play a vital role in the future power grid. Given the deployment of intermittent renewable energy sources and the ever increasing consumption of electricity, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. The literature is rich with...

Challenges in forecasting peak electricity demand

We want to forecast the peak electricity demand in a half-hour period in twenty years time. We have fifteen years of half-hourly electricity data, temperature data and some economic and demographic data. The location is South Australia: home to the most volatile electricity demand in the world.