eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms

Abstract

Electricity forecasting is crucial in scheduling and planning of future electric load, so as to improve the reliability and safeness of the power grid. Despite recent developments of forecasting algorithms in the machine learning community, there is a lack of general and advanced algorithms specifically considering requirements from the power industry perspective. In this paper, we present eForecaster, a unified AI platform including robust, flexible, and explainable machine learning algorithms for diversified electricity forecasting applications. Since Oct. 2021, multiple commercial bus load, system load, and renewable energy forecasting systems built upon eForecaster have been deployed in seven provinces of China. The deployed systems consistently reduce the average Mean Absolute Error (MAE) by 39.8% to 77.0%, with reduced manual work and explainable guidance. In particular, eForecaster also integrates multiple interpretation methods to uncover the working mechanism of the predictive models, which significantly improves forecasts adoption and user satisfaction.

TCCT: Tightly-coupled convolutional transformer on time series forecasting

“…LogSparse Transformer [23] and Informer [15] discover the sparsity of query-key matching matrix and they force the elements of query to attach to the partial elements of key for the sake of reducing the complexity. Autoformer [24], FEDformer [25] and ETSformer [26] combine the TSFT with seasonal-trend decomposition and signal processing method, e.g., Fourier Analysis, in attention mechanism to enhance their interpretability. Patch-wise attention is more popular and proven to be more useful recently.…”

“…Due to their simple architectures, it is convenient for them to combine with statistics models for the objective of improving their interpretability and forecasting capability. NBEATS [29] and DLinear [5] adopt seasonal-trend decomposition methods in their networks more concisely than FEDformer [25] but achieve better results in general. C. Challu et al [7] further presented N-HiTS that employs sampling and interpolation strategies on the basis of NBEATS for more precise and hierarchical prediction.…”

“…This problem is more noteworthy when it comes to some modified TSFT with hierarchical architecture in encoder. For instance, Informer [15] employs convolution layers between every two stages in encoder and FEDformer [25] keeps decomposing input sequences to acquire more precise seasonal features but neither of them apply the same operations to decoder and only the feature map of the last stage in encoder is sent to decoder. Crossformer [8] merges adjacent segments to obtain bigger patches in deeper stages in both encoder and decoder.…”

“…To handle the first redundant self-attention problem in decoder, we change the order of the self-attention and cross-attention in decoder. Thereby, before embarking upon deducing any relations within unknown prediction sequence, the prediction sequence receives the auto-regressive parts from the deepest encoder feature map, which serves as a better role for prediction sequence initialization before the first self-attention in decoder than simple zero-initialization with start token [15], randomly generated parameters [8], the trend decomposition of raw input sequence [25], and so forth. It is evident that the latter initialization formats of other TSFTs are either relatively simple or inefficient.…”