Day-ahead forecasting of grid carbon intensity in support of HVAC plant demand response decision-making to reduce carbon emissions
Lowry, GD (2018). Day-ahead forecasting of grid carbon intensity in support of HVAC plant demand response decision-making to reduce carbon emissions. Building Services Engineering Research and Technology.
Electrical HVAC loads in buildings are suitable candidates for use in demand response activity. This paper demonstrates a method to support planned demand response actions intended explicitly to reduce carbon emissions. Demand response is conventionally adopted to aid the operation of electricity grids and can lead to greater efficiency; here it is planned to target times of day when electricity is generated with high carbon intensity. Operators of HVAC plant and occupants of conditioned spaces can plan when to arrange shutdown of plant once they can foresee the opportune time of day for carbon saving. It is shown that the carbon intensity of the mainland UK electricity grid varies markedly throughout the day, but that this tends to follow daily and weekly seasonal patterns. To enable planning of demand response, 24-hour ahead forecast models of grid carbon intensity are developed that are not dependent on collecting multiple exogenous data sets. In forecasting half-hour periods of high carbon intensity either linear autoregressive or non-linear ANN models can be used, but a daily seasonal autoregressive model is shown to provide a 20% improvement in carbon reduction.
|Keywords||demand response; carbon intensity; autoregressive model; artificial neural network; 0905 Civil Engineering; 1202 Building; Building & Construction|
|Journal||Building Services Engineering Research and Technology|
|Digital Object Identifier (DOI)||doi:10.1177/0143624418774738|
|30 Apr 2018|
|Publication process dates|
|Deposited||16 Apr 2018|
|Accepted||11 Apr 2018|
|Accepted author manuscript|
CC BY 4.0
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