The Integrated Energy Management and Forecasting Dataset is a comprehensive data collection specifically designed for advanced algorithmic modeling in energy management. It combines two distinct yet complementary datasets - the Energy Forecasting Data and the Energy Grid Status Data - each tailored for different but related purposes in the energy sector.
This dataset proposes a new method of modelling dynamic loads based on instantaneous p-q theory, to be employed in large power system networks in a digital real time environment. In order to decrease the computational burden associated to the dynamic load modelling, a p-q- theory-based approach for load modelling is proposed in this dataset. This approach is based on the well-known p-q- instantaneous theory developed for power electronics converters, and it consts only of linear controllers and of a minimal usage of control loops, reducing the required computational power.
Dataset1
Conversion loss modeling plays a crucial role in hybrid AC/DC microgrid (MG) energy management (EM). However, accurate calculation of the conversion losses is often very costly. Additionally, existing surrogate models typically rely on fixed-voltage DC buses, leading to excessive voltage magnitudes. To overcome these limitations, we propose surrogate models based on piecewise linear neural networks (NNs) that estimate conversion losses using converter power and variable-voltage DC buses.
This dataset has EV charging data from 2019 to the present day. SFU's Burnaby campus currently has two different types of Electric Vehicle Charging Stations on campus. There is no additional charge to use the station; however, the Permit or Daily Rate required in each lot remains in effect for the EV Reserved stalls.
This dataset is created by an experimental setup of a DC-PV -Battery-based grid-connected distributed generation system. This dataset is split into four parts such as irradiance, and temperature, which were measured by a meteorological station, and lastly, PV output current and voltage acquired by an inverter. Furthermore, we can have a chance to obtain the output PV power by multiplying current and voltage. The dataset has 288 elements for one day as a time series since the station obtains the data within five minutes. However, the whole dataset has three days of data with 864 elements.
This paper presents a real-time reconfigurable Cyber-Power Grid Operation Testbed (CPGrid-OT) with multi-vendor, industry-grade hardware, and software.
This dataset is created for neural network-based surrogate modeling of the power conversion losses. The dataset includes four sets of data (for AC/DC conversion losses under inversion/rectification moes and DC/DC conversion losses during battery charging/discharging, respectively) for the neural network. The raw data is generated using high fidelity analytical models.
This Data is collected from a MW-size energy storage pilot system located on the Baoshan campus of National Changhua University of Education (NCUE). A significant amount of research is done through this in oreder to improve grid efficiency and stability, making important contributions to establish a green energy network in Taiwan.This dataset is a time-series changes in paramaters such as voltage , current , power factor and Kilowatt hour.
