One of the big concerns of big cities is facing with the increase of fossil fuel vehicles in the roads. The cars intensify greenhouse gas emission in civic centers. One approach to reduce the emission is replacing the cars by Plug-In Electric Vehicles (PEVs). In spite of reducing the emission, PEVs have some adverse effect on electric distribution networks. Technical challenges such as load and...

The speed profile of the train will be determined according to criteria such as safety, travel convenience, and the type of electric motor used for traction. Due to the passengers and cargo on the train, the electric train load is constantly changing. This will require reassigning the speed controller’s parameters of the electric train. For this purpose, the Gravitational Search optimization Al...

This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides additional degrees of freedom for handling more uncertainties for improving prediction a...

We consider the optimal load-shedding problem in electric power systems where a number of transmission lines are to be taken out of service. The nonlinear power flow equations and the binary decision variables lead to a mixed-integer nonlinear program. We show that the load-shedding problem has separable structure that can be exploited by using the alternating direction method of multipliers. W...

Any use of energy in industrial productive activities is combined with various environment impacts. Withintransportation, this fact was not only found among land transport, railways and maritime transport, but also in the air transport industry. An effective climate protection requires strategies and measures for reducing all greenhouses gas emissions, in particular carbon dioxide, and must tak...

Short term electrical load forecasting is critical in ensuring reliability and operational efficiency for electrical systems. With an influx of monitoring data and the growing technical complexity of the grid, there is a great interest and need for accurate forecasting in electricity planning. Our project uses a curated electric load dataset from Kaggle and evaluates the performance of several ...

In order to efficiently improve the prediction accuracy, two load forecasting model based on fuzzy theory are presented, which are fuzzy clustering model and improved fuzzy regression analysis model .The method of fuzzy clustering is used to divide the area by the similar feature of load increasing. The new division is promising to improve the result of evident degree of clustering index to pow...

Accurate daily peak load forecasts are important for secure and profitable operation of modern power utilities, with deregulation and competition demanding ever-increasing accuracies. Machine learning techniques including neural and abductive networks have been used for this purpose. Network committees have been proposed for improving regression and classification accuracy in many disciplines, ...

With the development of economy and the progress of science, the proportion of electrified railway load in the power gird has been keeping on increasing, which impacts the short-term forecasting in load a lot, therefore, it is very important to analyze short-term load of electrified railway forecasting. This paper analyzes the power gird load-forecasting considering the influence of the electri...