User profiles for Mashud Rana
 | Md Mashud RanaData61 | CSIRO Verified email at data61.csiro.au Cited by 1614 |
Correlation and instance based feature selection for electricity load forecasting
Appropriate feature (variable) selection is crucial for accurate forecasting. In this paper we
consider the task of forecasting the future electricity load from a time series of previous …
consider the task of forecasting the future electricity load from a time series of previous …
Univariate and multivariate methods for very short-term solar photovoltaic power forecasting
We consider the task of forecasting the electricity power generated by a solar PhotoVoltaic (PV)
system for forecasting horizons from 5 to 60 min ahead, from previous PV power and …
system for forecasting horizons from 5 to 60 min ahead, from previous PV power and …
Forecasting electricity load with advanced wavelet neural networks
M Rana, I Koprinska - Neurocomputing, 2016 - Elsevier
Electricity load forecasting is a key task in the planning and operation of power systems and
electricity markets, and its importance increases with the advent of smart grids. In this paper, …
electricity markets, and its importance increases with the advent of smart grids. In this paper, …
2D-interval forecasts for solar power production
Accurate prediction of the power generated from solar energy is required for the successful
integration of solar energy into the power grid. In this paper we consider forecasting the …
integration of solar energy into the power grid. In this paper we consider forecasting the …
Multiple steps ahead solar photovoltaic power forecasting based on univariate machine learning models and data re-sampling
… Author links open overlay panel Mashud Rana a , Ashfaqur Rahman b … Rana et al. [12] also
applied SVR for 30-min to 6-h ahead prediction of solar power output. However, in contrast to …
applied SVR for 30-min to 6-h ahead prediction of solar power output. However, in contrast to …
SSDNet: State space decomposition neural network for time series forecasting
In this paper, we present SSDNet, a novel deep learning approach for time series forecasting.
SSDNet combines the Transformer architecture with state space models to provide …
SSDNet combines the Transformer architecture with state space models to provide …
Automatic Classification of Sensors in Buildings: Learning from Time Series Data
Smart buildings are generally equipped with thousands of heterogeneous sensors and
control devices that impact the operation of their electrical systems. Analytical tools that aim to …
control devices that impact the operation of their electrical systems. Analytical tools that aim to …
[HTML][HTML] Hierarchical Semi-Supervised Approach for Classifying Activities of Workers Utilising Indoor Trajectory Data
Activity recognition refers to the process of automatically identifying or interpreting activities
of objects based on the data captured from different sensing devices. While previous …
of objects based on the data captured from different sensing devices. While previous …
A data-driven approach based on quantile regression forest to forecast cooling load for commercial buildings
M Rana, S Sethuvenkatraman… - Sustainable Cities and …, 2022 - Elsevier
Reliable prediction of thermal load is essential for implementing an efficient and economic
energy management plan in commercial buildings. While previous research has been …
energy management plan in commercial buildings. While previous research has been …
Machine learning approach to investigate the influence of water quality on aquatic livestock in freshwater ponds
… Author links open overlay panel Mashud Rana a , Ashfaqur Rahman b , Joel Dabrowski c , …
Rana, Rahman, Hugo, McCulloch, and Hellicar (2020) applied four ML models including NNs …
Rana, Rahman, Hugo, McCulloch, and Hellicar (2020) applied four ML models including NNs …