xcy123987

research_experiments_of_electrical_load_forecasting

This is a code repository of research of load forecasting

Load-Forecasting-using-Deep-Neural-Networks

Short-Term-Electric-Load-Forecasting-CSL

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 .

AxeWatt

AxeWatt is the main aplication for forecasting energy load.

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

wesm-rtd

Wholesale Electricity Market (WESM) price forecasting tool for short-term forecasting of Load-Weighted Average Price

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

Electric-Load-Forecasting-Final-Project

django-machine-learning-load-forecasting

Load-Forecasting-based-on-ML

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.

DSAI-HW1-Peak-Load-Forecasting

Load-Forecasting-using-SVM

peak-load-forecasting-ml

peak-load-forecasting-dl

enduse

Development end-use model for load forecasting

EV-station-load-forecasting

Electrical-Load-Forecasting-with-various-algorithms

Electrical load forecasting with various regression methods

ann

A simple ANN based load forecasting model

Load-Forecast

slimply forecast elcetric load

pshforc

DNN for load forecast

NTPP

BTP Project : Network Traffic Prediction || A probabilistic deep machinery that models the traffic characteristics of hosts on a network and effectively forecasts the network traffic patterns, such as load spikes.