Diffusion Convolutional Recurrent Neural Network-Based Load Forecasting During COVID-19 Pandemic Situation

Infected by the novel coronavirus (COVID-19 – C-19) pandemic, worldwide energy
generation and utilization have altered immensely. It remains unfamiliar in any case that
traditional short-term load forecasting methodologies centered upon single-task, singlearea,
and standard signals could precisely catch the load pattern during the C-19 and must
be cautiously analyzed. An effectual administration and finer planning by the power
concerns remain of higher importance for precise electrical load forecasting. There presents
a higher degree of unpredictability’s in the load time series (TS) that remains arduous in
doing the precise short-term load forecast (SLF), medium-term load forecast (MLF), and
long-term load forecast (LLF). For excerpting the local trends and capturing similar patterns
of short and medium forecasting TS, we proffer Diffusion Convolutional Recurrent Neural
Network (DCRNN), which attains finer execution and normalization by employing
knowledge transition betwixt disparate forecasting jobs. This as well evens the portrayals
if many layers remain stacked. The paradigms have been tested centered upon the actual
life by performing comprehensive experimentations for authenticating their steadiness and
applicability. The execution has been computed concerning squared error, Root Mean
Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error
(MAE). Consequently, the proffered DCRNN attains 0.0534 of MSE in the Chicago area,
0.1691 of MAPE in the Seattle area, and 0.0634 of MAE in the Seattle area.