MSMVAN: Multi Step Multi Variate Deep Attention Network for Renewable Energy Forecast

The importance of renewable energy in our everyday lives cannot be overstated, specifically highlighting solar and wind energies as two crucial sustainable power sources. Nevertheless, it is crucial to acknowledge that the amount of energy derived from renewable sources is subjected to variability, being influenced by environmental conditions. To ensure efficient management of energy load demands in diverse applications and enhance the performance of storage grids, precise energy production forecasting is vital. However, existing models doesn't recognise the important variables for forecasting and fails in the presence of noisy or missing data. To address these issues, we proposed a MultiStep Multi Variate Deep Attention Network (MSMVAN) model with masked attention and dilated Fourier convolutions. The proposed approach utilizes a sequential model to analyze non-linear relationships in time series data. Further, dilated fourier convolutions are incorporated to capture multistep spatial and cross correlations, while attention mechanisms are employed to identify significant variables. As a result, the model acquires improved vector representations, leading to enhanced energy forecasting power. We evaluated our model on three standard datasets and from the experimental results we conclude that our proposed model outperforms the state-of-the-art models in forecasting solar and wind energy.