xcy123987's starred repositories
research_experiments_of_electrical_load_forecasting
This is a code repository of research of load forecasting
load_forecast
load forecast using ML techniques
transfer-learning-forecasting
The repository for load forecasting through Transfer Learning techniques
Electrical-Load-forcasting
It's long term load forecasting for my college .
XGBoost-Stock-Price-Prediction
This script loads desired stock price training data, trains an XGBoost Regressor for Time Series Forecasting (allowing fine-tuning) and downloads the model to be used for prediction tasks.
tdm-power-load-forecaster
Power load forecasting application for the TDM-Edge devices
Load-Forecasting-with-DeepNN
Implementation of two different models (TF2/Keras) from literature and a custom model for day-ahead load forecasting (short term load forecasting) on two different datasets.
Cyber-Attack-Robust-Load-Forecasting
Exploratory project
forecast-nyc-demand
Predicting NYC electric load using timeseries forecasting techniques
Basopra
BASOPRA - BAttery Schedule OPtimizer for Residential Applications. Daily battery schedule optimizer (i.e. 24 h optimization framework), assuming perfect day-ahead forecast of the electricity demand load and solar PV generation in order to determine the maximum economic potential regardless of the forecast strategy used. Include the use of different applications which residential batteries can perform from a consumer perspective. Applications such as avoidance of PV curtailment, demand load-shifting and demand peak shaving are considered along with the base application, PV self-consumption. Different battery technologies and sizes can be analyzed as well as different tariff structures. Aging is treated as an exogenous parameter, calculated on daily basis and is not subject of optimization. Data with 15-minute temporal resolution are used for simulations. The model objective function have two components, the energy-based and the power-based component, as the tariff structure depends on the applications considered, a boolean parameter activate the power-based factor of the bill when is necessary.
Electrical-Load-Forecasting-Using-Deep-Learning-
Electrical Load of SGGS college was forecasted for four years that is (2020-2024). To get rough idea about electrical load in upcoming years for infrastructure planning and development inside college campus.
Electricity-Load-Forecasting-for-Smart-Grids-
The project aims to create a precise machine-learning model to forecast energy demand based on weather patterns, historical consumption patterns, and generation data.
Electrical-Load-Forecasting-with-various-algorithms
Electrical load forecasting with various regression methods
Load-Forecast
slimply forecast elcetric load