Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed-integer programming formulation for load shifting ...
This study shows that proper preprocessing and feature engineering techniques are crucial for high performance of the predictive models.Kodikara, Gayantha R. L.McHenry, Lindsay J.Univ Wisconsin Dept Geosci 3209 N Maryland Ave Milwaukee WI 53211 USAIcarus: International Journal of Solar System ...
Another promising solution is Transfer Learning, which applies pre-trained models on new problems [22]. However, it remains impractical when no pre-trained models are available locally. Using pre-trained models from other regions is highly risky since the characteristics of citizens and their ...
In Section 4, simulation results of the proposed method are represented compared with the conventional methods. Finally, concluding remarks are provided in Section 5. 2. System Model The typical and proposed NILM system models are configured in Figure 1a,b, respectively. The typical NILM system ...
The benefit of using RRS is that it has been classified as an essential climate variable vital in inferring optically active agents of water [9,10]. A concise background and history of the FUI system is well-documented in open access literature [6,11]. Natural waters absorb and scatter ...
Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models. Renew. Sustain. Energy Rev. 2014, 31, 509–519. [CrossRef] 61. Yadav, A.K.; Chandel, S.S. Solar radiation prediction using Artificial Neural Network techniques: A ...
Here, we successfully classify four different carbon fiber fabrics using a simple, defined quantification method with the strong classification capacity of machine learning techniques. Knowledge-based machine learning models were developed after the training process based on a large number of experimental ...