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With the advent of smart grid, load forecasting is emerging as an essential technology to implement optimal planning and control of grid assets. Ergo in recent years, a significant thrust can be witnessed for the research towards the improvement of the prediction of the energy demand. However thus far there has not been any one technique in the literature that is shown to give best forecasts for a variety of sites; almost all the papers published on load forecasting, report their best results on just one of the dataset. This problem accentuates further when the training data does not have enough data points to learn patterns over all the seasons. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, especially when the training data is limited, is a big challenge, which is addressed in this paper. The paper presents a novel combination of deep learning with feature engineering for short-term load forecasting. The proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), is based on the sequential model in conjunction with the hand-crafted derived features in order to aid the model for better learning and predictions. The raw data and the hand-crafted features are trained at separate levels, then their respective outputs are combined to make the final prediction. The efficacy and robustness of the proposed methodology is evaluated on diverse datasets from five countries with completely different patterns. The extensive experiments and results demonstrate that the proposed technique is superior to the existing state of the art.

A Novel Technique for Short-Term Load Forecasting Using Sequential Models and Feature Engineering