Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting

Bayesian Deep Learning | Uncertainty in Deep Learning

Residential Power Load Forecasting

The prepaid electric power metering market is being driven in large part by advancements in and the adoption of Smart Grid technology. Advanced smart meters facilitate the deployment of prepaid systems with smart prepaid meters. A successful program hinges on the ability to accurately predict the amount of energy consumed on a daily basis for each end user. This method of forecasting is called ...

A Deep Learning Framework for Short-term Power Load Forecasting

The scheduling and operation of power system becomes prominently complex and uncertain, especially with the penetration of distributed power. Load forecasting matters to the effective operation of power system. This paper proposes a novel deep learning framework to forecast the short-term grid load. First, the load data is processed by Box-Cox transformation, and two parameters (electricity pri...

Electricity Load Forecasting Using Machine Learning Techniques

Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...

Electricity Load Forecasting Using Machine Learning Techniques

Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...