Pinned Repositories
cvlearning
day
https-github.com-think-iee-expresscompany
jvm
Load-Forecasting-using-Different-Deep-Learning-Architectures
this project is to implement different deep learning architectures and evaluate them based on their performance on the hour-ahead electricity price and load prediction task. More specifically, we will evaluate (i) Random Forest, (ii) CNN-Univariate, (iii) CNN-Multivariate, (iv) RNN-LSTM and (v) BiLSTM architectures, using the root mean squared error (RMSE). Furthermore, we will experiment on different task formulations and types of frameworks, alongside the two following dimensions: • We will compare the performance of univariate time series forecasting and multivariate time series forecasting. Univariate time series forecasting is a framework on which the predicted quantity (i.e. electricity price) is the sole feature that is used by the models, whereas the multivariate variant of the task also uses other features which may prove important for the prediction, such as the load of the energy grid, the temperature, etc. • We will compare the performance of using different time-steps (3, 10 and 25 time-lags) as a way of reframing the time-series prediction task into a supervised learning problem, i.e. using the past 3, 10 and 25 values of the features which are fed into our models.
SecureCloudStorage
甘肃电网，安全云存储项目。
SecureCloudStorage_master
SecureCloudStorageVue
安全云存储项目，前端Vue。
StreamCollectionVisualization
流式采集技术项目，数据可视化
wl-explorer
用于vue框架的文件管理器插件，云盘、网盘。File manager plug-in for vue framework, cloud disk.
shijichen-wilf's Repositories
shijichen-wilf/jvm
shijichen-wilf/cvlearning
shijichen-wilf/day
shijichen-wilf/https-github.com-think-iee-expresscompany
shijichen-wilf/Load-Forecasting-using-Different-Deep-Learning-Architectures
this project is to implement different deep learning architectures and evaluate them based on their performance on the hour-ahead electricity price and load prediction task. More specifically, we will evaluate (i) Random Forest, (ii) CNN-Univariate, (iii) CNN-Multivariate, (iv) RNN-LSTM and (v) BiLSTM architectures, using the root mean squared error (RMSE). Furthermore, we will experiment on different task formulations and types of frameworks, alongside the two following dimensions: • We will compare the performance of univariate time series forecasting and multivariate time series forecasting. Univariate time series forecasting is a framework on which the predicted quantity (i.e. electricity price) is the sole feature that is used by the models, whereas the multivariate variant of the task also uses other features which may prove important for the prediction, such as the load of the energy grid, the temperature, etc. • We will compare the performance of using different time-steps (3, 10 and 25 time-lags) as a way of reframing the time-series prediction task into a supervised learning problem, i.e. using the past 3, 10 and 25 values of the features which are fed into our models.
shijichen-wilf/SecureCloudStorage
甘肃电网，安全云存储项目。
shijichen-wilf/SecureCloudStorage_master
shijichen-wilf/SecureCloudStorageVue
安全云存储项目，前端Vue。
shijichen-wilf/StreamCollectionVisualization
流式采集技术项目，数据可视化
shijichen-wilf/wl-explorer
用于vue框架的文件管理器插件，云盘、网盘。File manager plug-in for vue framework, cloud disk.