Forecasting energy demand using neural-network-based grey residual modification models
提出将神经网络与灰色残差修正模型结合，解决传统GM(1,1)模型参数难确定的问题，用实际电力与能源需求数据验证了预测精度提升。
Grey forecasting models have taken an important role for forecasting energy demand, particularly the GM(1,1) model, because they are able to construct a forecasting model using a limited samples without statistical assumptions. To improve prediction accuracy of a GM(1,1) model, its predicted values are often adjusted by establishing a residual GM(1,1) model, which together form a grey residual modification model. Two main issues should be considered: the sign estimation for a predicted residual and the way the two models are constructed. Previous studies have concentrated on the former issue. However, since both models are usually established in the traditional manner, which is dependent on a specific parameter that is not easily determined, this paper focuses on the latter issue, incorporating the neural-network-based GM(1,1) model into a residual modification model to resolve the drawback. Prediction accuracies of the proposed neural-network-based prediction models were verified using real power and energy demand cases. Experimental results verify that the proposed prediction models perform well in comparison with original ones.