Plug-in hybrid electric vehicles (PHEVs) have a large potential to reduce greenhouse gases emissions and increase fuel economy and fuel flexibility. PHEVs are propelled by the energy from both gasoline and electric power sources. Penetration of PHEVs into the automobile market affects the electrical grid through an increase in electricity demand. This paper studies effects of the wide spread ad...

Due to the rapid promotion of electric vehicles, large-scale charging behavior vehicles brings a large number time and space highly random load, which will have great impact on safe operation distribution network. This paper proposes planning method vehicle station based travel data. Firstly, didi trip data is processed mined get matrix other information. Then, load forecasting model establishe...

The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the load performance EV load, a corresponding model-based multi-channel convolutional neural network and temporal (MCCNN-TCN) are proposed. (MCCNN) can extract fluctuation characteristics at various time scales, while (TCN) build time-series dependence between forecas...

Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedd...

Accurate demand forecasts are important for managing energy efficiently in electric grids. However, building models for demand forecasting is a challenging task as it depends on numerous factors that are both intrinsic and external to the grid. Furthermore, these factors are time-varying and non-linear as well. This makes demand forecasting a cumbersome task. This investigation proposes a simpl...

Composite load model is developed for 1-24 hours ahead prediction of hourly electric loads. The load model is composed of three components : the nominal load, the type load and the residual load. The nominal load is modeled such that the Kalman filter can be used and the parameters of the model are adapted by the exponentially weighted recursive least squares method. The type load component is ...

The electricity consumption of a terminal is mainly related to the number of container movements and the weather of each day. With the introduction of electric mobility for heavy duty container carriers at a seaport container terminal short-term load forecasting gains an important part in the procurement process. This paper describes a case-based approach to the forecasting of the electricity c...

A global model is presented for short-term electric load forecasting using artificial neural networks. The model predicts the complete curve of the 24 hourly values for the next day. The development of this model consists of three phases: a prior one, in which, starting from historical data, each day is classified according to its load profile by means of self-organising feature maps; the secon...