Providing accurate electric load forecasting results plays a crucial role in daily energy management of the power supply system. Due to superior forecasting performance, the hybridizing support vector regression (SVR) model with evolutionary algorithms has received attention and deserves to continue being explored widely. The cuckoo search (CS) algorithm has the potential to contribute more satisfactory electric load forecasting results. However, the original CS algorithm suffers from its inherent drawbacks, such as parameters that require accurate setting, loss of population diversity, and easy trapping in local optima (i.e., premature convergence). Therefore, proposing some critical improvement mechanisms and employing an improved CS algorithm to determine suitable parameter combinations for an SVR model is essential. This paper proposes the SVR with chaotic cuckoo search (SVRCCS) model based on using a tent chaotic mapping function to enrich the cuckoo search space and diversify the population to avoid trapping in local optima. In addition, to deal with the cyclic nature of electric loads, a seasonal mechanism is combined with the SVRCCS model, namely giving a seasonal SVR with chaotic cuckoo search (SSVRCCS) model, to produce more accurate forecasting performances. The numerical results, tested by using the datasets from the National Electricity Market (NEM, Queensland, Australia) and the New York Independent System Operator (NYISO, NY, USA), show that the proposed SSVRCCS model outperforms other alternative models.

Multimodal recommendation systems have made significant progress by leveraging graph convolutional networks to integrate user behavior with item content, including images and text. However, these systems still encounter two major challenges: noise edges in interaction graphs and noise in multimodal features of items. Existing works tend to address only one type of noise problem to enhance recommendation performance. This article proposes a new Dual Denoising Multimodal Graph Recommendation (DDRec) model, designed to enhance multimodal recommendation systems by tackling both challenges simultaneously. Specifically, we design two denoising techniques: hard denoising and soft denoising. For noise edges in interaction graphs, the hard denoising method uses preference scores of user nodes and item nodes in different modality interaction graphs as edge weights and prunes edges below a certain threshold to eliminate noise. For noise in multimodal features, the soft denoising method leverages item and item semantic graph information to denoise modal features, thus obtaining modality features related to user preferences. Finally, we employ contrastive learning to compare user and item representations derived from the denoised modality interaction graphs against those from the original graph, ensuring the consistency of nodes across various views. Our comprehensive experiments across four public datasets validate the enhanced performance and effectiveness of the DDRec model.