Forecasting Regional Energy Consumption via Jellyfish Search-Optimized Convolutional-Based Deep Learning

The energy sector must achieve a delicate balance between energy supply and demand. Highly accurate energy consumption forecasts can help plant operators achieve this goal. In this study, various techniques from three artificial intelligence categories, namely, convolutional neural networks (CNNs), machine learning (ML), and time-series deep learning (DL), were applied to predict short-term regional energy consumption from a power company. An image conversion process was proposed to utilize the powerful image-processing capabilities of CNNs, in which numerical matrices of input values created by the sliding-window technique are encoded into two-dimensional grayscale images as input for CNN models. The proposed method outperformed conventional numerical input methods. Then, the best ML and time-series DL models were combined into ensemble models with ImageNet-winning CNN architectures based on the rationale of utilizing the strengths of each approach and compensating for their limitations. However, according to the numerical experiments for the regional energy consumption prediction, the ensemble models had lower performance than CNNs alone. Various leading models performing single-step or multistep forecasting and models that use seasonal or annual data were constructed. The performance of all the constructed models was evaluated, and the most efficient models were determined as proposed models. Finally, a nature-inspired optimization algorithm, Jellyfish Search (JS), was used to fine-tune the proposed models' hyperparameters to minimize prediction error. The results revealed that the fine-tuning of hyperparameters via the JS algorithm significantly enhanced the precision of the CNN models, resulting in a mean absolute percentage error (MAPE) improvement range of 4.4% to 17.5% compared to the default models. The proposed models can help utilities plan daily power allocation and match supply and demand to maintain a stable power system.