A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning
This paper aims at studying the data-driven short-term provincial load forecasting (STLF)
problem via an in-depth exploration of benefits brought by the feature engineering and …
problem via an in-depth exploration of benefits brought by the feature engineering and …
Robust deep Gaussian process-based probabilistic electrical load forecasting against anomalous events
The abnormal events, such as the unprecedented COVID-19 pandemic, can significantly
change the load behaviors, leading to huge challenges for traditional short-term forecasting …
change the load behaviors, leading to huge challenges for traditional short-term forecasting …
Data analytics and optimization of an ice-based energy storage system for commercial buildings
Ice-based thermal energy storage (TES) systems can shift peak cooling demand and reduce
operational energy costs (with time-of-use rates) in commercial buildings. The accurate …
operational energy costs (with time-of-use rates) in commercial buildings. The accurate …
Modeling and optimization of time-of-use electricity pricing systems
YC Hung, G Michailidis - IEEE Transactions on Smart Grid, 2018 - ieeexplore.ieee.org
Time-of-use (TOU) pricing is an important strategy for electricity providers to manage supply
and make the grid more efficient; as well as for consumers seeking to manage their costs. In …
and make the grid more efficient; as well as for consumers seeking to manage their costs. In …
Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand
Load forecasting could play a crucial role in energy management and control of buildings in
residential neighborhoods. In these areas, electricity demand is influenced by different …
residential neighborhoods. In these areas, electricity demand is influenced by different …
Limref: Local interpretable model agnostic rule-based explanations for forecasting, with an application to electricity smart meter data
Accurate electricity demand forecasts play a key role in sustainable power systems. To
enable better decision-making especially for demand flexibility of the end-user, it is …
enable better decision-making especially for demand flexibility of the end-user, it is …
Smart-meter big data for load forecasting: An alternative approach to clustering
Accurate forecasting of electricity demand is vital to the resilient management of energy
systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy …
systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy …
A compositional kernel based gaussian process approach to day-ahead residential load forecasting
Load forecasting is an expected ability of electric power networks to enable effective
capacity planning. This paper proposes a probabilistic approach to short-term load …
capacity planning. This paper proposes a probabilistic approach to short-term load …
Energy consumption model with energy use factors of tenants in commercial buildings using Gaussian process regression
YR Yoon, HJ Moon - Energy and Buildings, 2018 - Elsevier
Identification of the factors influencing energy consumption in buildings is crucial for energy
efficient control in the operation stage. By using a multi-variate approach in energy …
efficient control in the operation stage. By using a multi-variate approach in energy …
Probabilistic forecasting of electricity demand incorporating mobility data
Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic,
utilities and grid operators worldwide face unprecedented challenges. These unanticipated …
utilities and grid operators worldwide face unprecedented challenges. These unanticipated …