Electric vehicles are anticipated to be essential components of future energy systems, as they possess the capability assimilate surplus generated by renewable sources. With increasing popularity plug-in hybrid electric (PHEVs), conventional internal combustion engine (ICE)-based expected gradually phased out, thereby decreasing greenhouse gases and reliance on foreign oil. Intensive research d...

This work brings together and applies a large representation of the most novel forecasting techniques, with origins applications in other fields, to short-term electric load problem. We present comparison study between different classic machine learning deep techniques recent methods for data-driven analysis dynamical models (dynamic mode decomposition) ensemble applied forecasting. explores in...

Abstract The increasing dependency on electricity and demand for renewable energy sources means that distributed system operators face new challenges in their grid. Accurate forecasts of electric load can solve these challenges. In recent years deep neural networks have become increasingly popular research, researchers carried out many experiments to create the most accurate learning models. Pl...

Power transformers are important and expensive components in the electric power system. The knowledge of the actual status of the transformer insulation behavior, load tap changer performance, temperature, and load condition is necessary in order to evaluate the service performance concerning reliability, availability and safety. Systems abnormalities, loading, switching and ambient condition n...

There are a lot of uncertainties in planning and operation of electric power system, which is a complex, nonlinear, and non-stationary system. Advanced computational methods are required for planning and optimization, fast control, processing of field data, and coordination across the power system for it to achieve the goal to operate as an intelligent smart power grid and maintain its operatio...

We discuss the application of an end-use load shape estimation technique to develop annual energy use intensities (EUIs) and hourly end-use load shapes (LSs) for commercial buildings in the Pacific Gas and Electric Company (PG&E) service territory. The results will update inputs for the commercial sector energy and peak demand forecasting models used by PG&E and the California Energy Commission...

With the construction of modern power system, load forecasting is significant to keep electric Internet Things in operation. However, it usually needs collect massive data on server and may face problem privacy leakage raw data. Federated learning can enhance clients by frequently transmitting model updates. Concerning increasing communication burden resource-heterogeneous resulting from freque...

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts...

Load forecasting in power systems is an important subject and has been studied from different points of view in order to achieve better load forecasting results. ”Ius paper will address one of the challenges in spatial load forecasting area urban re-development, and present a theory and methodology to incorporate urban re-development into spatial load forecasting considerations.