Published:
Australian distribution networks are planning for an era in which a large share of households charge an EV at home. The interesting question is not just “how much extra energy?” but “when does that energy get drawn, and how does the shape of the residential load change?”
Published:
Defect detection on manufactured parts is a classic anomaly detection problem: most items are normal, defects are rare, and the visual signature of a defect varies from batch to batch. A common practical trick is to extract a set of local features from an image and score how “unusual” the set looks compared with clean examples.
Published:
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Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Published:
Defect detection on manufactured parts is a classic anomaly detection problem: most items are normal, defects are rare, and the visual signature of a defect varies from batch to batch. A common practical trick is to extract a set of local features from an image and score how “unusual” the set looks compared with clean examples.
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Published:
Residential electricity forecasting sits between two hard problems. Household-level load is noisy and non-stationary, while feeder-level load is smoother but loses the behavioural signal that drives the peaks. Models trained on one level of aggregation often generalise badly to another.
Published:
Australian distribution networks are planning for an era in which a large share of households charge an EV at home. The interesting question is not just “how much extra energy?” but “when does that energy get drawn, and how does the shape of the residential load change?”
Published:
Australian distribution networks are planning for an era in which a large share of households charge an EV at home. The interesting question is not just “how much extra energy?” but “when does that energy get drawn, and how does the shape of the residential load change?”
Published:
Distribution networks in Australia are seeing more electric vehicles every year, and network operators need to know, in near real time, when and where charging events happen. Smart-meter data offers the raw signal, but EV charging looks a lot like other high-power appliances (heat pumps, kettles, ovens) once the meter aggregates everything at the household level.
Published:
Residential electricity forecasting sits between two hard problems. Household-level load is noisy and non-stationary, while feeder-level load is smoother but loses the behavioural signal that drives the peaks. Models trained on one level of aggregation often generalise badly to another.
Published:
Residential electricity forecasting sits between two hard problems. Household-level load is noisy and non-stationary, while feeder-level load is smoother but loses the behavioural signal that drives the peaks. Models trained on one level of aggregation often generalise badly to another.
Published:
Australian distribution networks are planning for an era in which a large share of households charge an EV at home. The interesting question is not just “how much extra energy?” but “when does that energy get drawn, and how does the shape of the residential load change?”
Published:
Defect detection on manufactured parts is a classic anomaly detection problem: most items are normal, defects are rare, and the visual signature of a defect varies from batch to batch. A common practical trick is to extract a set of local features from an image and score how “unusual” the set looks compared with clean examples.
Published:
Residential electricity forecasting sits between two hard problems. Household-level load is noisy and non-stationary, while feeder-level load is smoother but loses the behavioural signal that drives the peaks. Models trained on one level of aggregation often generalise badly to another.
Published:
Defect detection on manufactured parts is a classic anomaly detection problem: most items are normal, defects are rare, and the visual signature of a defect varies from batch to batch. A common practical trick is to extract a set of local features from an image and score how “unusual” the set looks compared with clean examples.
Published:
Distribution networks in Australia are seeing more electric vehicles every year, and network operators need to know, in near real time, when and where charging events happen. Smart-meter data offers the raw signal, but EV charging looks a lot like other high-power appliances (heat pumps, kettles, ovens) once the meter aggregates everything at the household level.
Published:
Distribution networks in Australia are seeing more electric vehicles every year, and network operators need to know, in near real time, when and where charging events happen. Smart-meter data offers the raw signal, but EV charging looks a lot like other high-power appliances (heat pumps, kettles, ovens) once the meter aggregates everything at the household level.
Published:
Distribution networks in Australia are seeing more electric vehicles every year, and network operators need to know, in near real time, when and where charging events happen. Smart-meter data offers the raw signal, but EV charging looks a lot like other high-power appliances (heat pumps, kettles, ovens) once the meter aggregates everything at the household level.