title: Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation

authors: del Campo-Ávila, José

subjects: computer science

abstract: A new methodology to predict one-day-ahead hourly solar global radiation is proposed in this paper. This information is very useful to address many real problems; for instance, energy-market decision making is one of the contexts where that information is essential to ensure the correct integration of grid-connected photovoltaic solar systems. The developed methodology is based on the contribution of different experts to obtain improved data-driven models when included in the data mining process. The modelling phase, when models are induced and new patterns can be identified, is the one that most benefits from that expert knowledge. In this case, it is achieved by combining clustering, regression and classification methods that exploit meteorological data (directly measured or predicted by weather services). The developed models have been embedded in a prediction system that offers reliable forecasts on next-day hourly global solar radiation. As a result of the automatic learning process including the knowledge of different experts, 14 different types of day were identified based on the shape of hourly solar radiation throughout a day. The conventional definitions of types of days, that usually consider 4 options, are updated with this new proposal. The next-day prediction of hourly global radiation is obtained in two phases: in the first one, the next-day type is obtained from among the 14 possible types of day; in the second one, values of hourly global radiation are obtained using the centroid of the predicted type of day and extraterrestrial solar radiation. The relative root mean square error of the prediction model is less than 20%, meaning a significant reduction compared to previous models. Moreover, the proposed models can be recognized in the context of eXplainable Artificial Intelligence.

text: Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation A new methodology to predict one-day-ahead hourly solar global radiation is proposed in this paper. This information is very useful to address many real problems; for instance, energy-market decision making is one of the contexts where that information is essential to ensure the correct integration of grid-connected photovoltaic solar systems. The developed methodology is based on the contribution of different experts to obtain improved data-driven models when included in the data mining process. The modelling phase, when models are induced and new patterns can be identified, is the one that most benefits from that expert knowledge. In this case, it is achieved by combining clustering, regression and classification methods that exploit meteorological data (directly measured or predicted by weather services). The developed models have been embedded in a prediction system that offers reliable forecasts on next-day hourly global solar radiation. As a result of the automatic learning process including the knowledge of different experts, 14 different types of day were identified based on the shape of hourly solar radiation throughout a day. The conventional definitions of types of days, that usually consider 4 options, are updated with this new proposal. The next-day prediction of hourly global radiation is obtained in two phases: in the first one, the next-day type is obtained from among the 14 possible types of day; in the second one, values of hourly global radiation are obtained using the centroid of the predicted type of day and extraterrestrial solar radiation. The relative root mean square error of the prediction model is less than 20%, meaning a significant reduction compared to previous models. Moreover, the proposed models can be recognized in the context of eXplainable Artificial Intelligence.

year: 2021

title: Cow breed, stocking rate, and calving date affect the profitability of pasture-based dairy farms

authors: Spaans, Olivia Katherine

abstract: Data from two multi-year experiments undertaken at the DairyNZ research farm, No 2. Dairy were collated and analysed. The effects of:
1)	 Breed (Jersey or Holstein-Friesian; JER, HF) at optimum or high comparative stocking rates (CSR; 80 or 100 kg body weight (BW)/t dry matter (DM) of feed available; CSR80 and CSR100, respectively); and 
2)	Changing the mean calving date (January, April, July, or October; JAN, APR, JUL, OCT) 
on biophysical measurements (i.e., milk production, pasture growth) and farm profitability were determined. 
Changing these strategic management variables affected the amount of pasture grown and consumed. For example, there was an interaction between breed and CSR in many of the measured pasture and milk production variables in experiment one; whilst, in experiment two, changes in the month of calving affected pasture dry matter intake (DMI) in early-mid and mid-late lactation, and annual milk production. Annual pasture production was greatly reduced at CSR100 on the HF farmlet, but not on the JER farmlet. Month of calving affected pasture DMI during early-mid lactation and mid-late lactation. 
A breed x CSR interaction reduced milk production per cow at CSR100, an effect that was greater in the HF breed than in the JER. Month of calving affected milk production per cow with the JUL herd producing the highest yield, compared with the JAN, APR and OCT herds.
Other breed x CSR interactions were also detected: JER cows had the lowest mean days in milk (DIM) to first heat at both CSR80 and 100; furthermore, there were negative effects of an increase in CSR from 80 to 100 kg body weight/t DM feed in the HF breed on DIM to first heat. Total metabolisable energy (ME) requirements per cow was affected by a breed x CSR interaction. At CSR80, the HF used more ME per cow than the JER, whilst at CSR100, both breeds used less total ME per cow than at CSR80, but the HF again used more than the JER.
From a profitability perspective, HF cows had a greater operating profit per hectare than JER at CSR80; however, JER cows were more profitable at CSR100. The JUL herd had the most profitable farm system, in both the base economic model and stochastic model. Results of the stochastic modelling with no premium included in the milk payment (NZ$/kg fat and protein) variable revealed the operating profit per hectare was greatest for the JUL herd, compared with the JAN, APR, and OCT herds. Inclusion of a premium for milk supplied during 16 May to 15 July in the milk payment (NZ$/ kg fat and protein) variable as well as a downward adjustment in milk price for JUL calving cows because of the high milk production peak in spring did not overcome this difference in profitability. Therefore, the JUL calving scenario was most profitable. The APR seasonal calving strategy resulted in a 10% reduction in operating profit per hectare, compared with the JUL herd, while the JAN and OCT strategy had the lowest operating profit per hectare.

text: Cow breed, stocking rate, and calving date affect the profitability of pasture-based dairy farms Data from two multi-year experiments undertaken at the DairyNZ research farm, No 2. Dairy were collated and analysed. The effects of:
1)	 Breed (Jersey or Holstein-Friesian; JER, HF) at optimum or high comparative stocking rates (CSR; 80 or 100 kg body weight (BW)/t dry matter (DM) of feed available; CSR80 and CSR100, respectively); and 
2)	Changing the mean calving date (January, April, July, or October; JAN, APR, JUL, OCT) 
on biophysical measurements (i.e., milk production, pasture growth) and farm profitability were determined. 
Changing these strategic management variables affected the amount of pasture grown and consumed. For example, there was an interaction between breed and CSR in many of the measured pasture and milk production variables in experiment one; whilst, in experiment two, changes in the month of calving affected pasture dry matter intake (DMI) in early-mid and mid-late lactation, and annual milk production. Annual pasture production was greatly reduced at CSR100 on the HF farmlet, but not on the JER farmlet. Month of calving affected pasture DMI during early-mid lactation and mid-late lactation. 
A breed x CSR interaction reduced milk production per cow at CSR100, an effect that was greater in the HF breed than in the JER. Month of calving affected milk production per cow with the JUL herd producing the highest yield, compared with the JAN, APR and OCT herds.
Other breed x CSR interactions were also detected: JER cows had the lowest mean days in milk (DIM) to first heat at both CSR80 and 100; furthermore, there were negative effects of an increase in CSR from 80 to 100 kg body weight/t DM feed in the HF breed on DIM to first heat. Total metabolisable energy (ME) requirements per cow was affected by a breed x CSR interaction. At CSR80, the HF used more ME per cow than the JER, whilst at CSR100, both breeds used less total ME per cow than at CSR80, but the HF again used more than the JER.
From a profitability perspective, HF cows had a greater operating profit per hectare than JER at CSR80; however, JER cows were more profitable at CSR100. The JUL herd had the most profitable farm system, in both the base economic model and stochastic model. Results of the stochastic modelling with no premium included in the milk payment (NZ$/kg fat and protein) variable revealed the operating profit per hectare was greatest for the JUL herd, compared with the JAN, APR, and OCT herds. Inclusion of a premium for milk supplied during 16 May to 15 July in the milk payment (NZ$/ kg fat and protein) variable as well as a downward adjustment in milk price for JUL calving cows because of the high milk production peak in spring did not overcome this difference in profitability. Therefore, the JUL calving scenario was most profitable. The APR seasonal calving strategy resulted in a 10% reduction in operating profit per hectare, compared with the JUL herd, while the JAN and OCT strategy had the lowest operating profit per hectare.

year: 2018

title: Radiocarbon dates on desiccated moa (Dinornithiformes) flesh from Inland Otago, New Zealand

authors: Anderson, Atholl

subjects: New Zealand

abstract: Remains of soft tissues from extinct moa (Dinornithiformes), mainly desiccated sinew, muscle, skin, and feathers have been recovered rather seldom but their distribution is distinctive. Of 22 records of such finds accumulated between 1864 and 1987, 15 came from inland Otago (Anderson 1989: 67–68, Table 5.2), west of Dunedin in the southern South Island. Most were found in the late nineteenth century (13 records) in rockshelters, clefts or alluvial sediments and were regarded at the time as evidence of the survival of moa up to about AD 1800 (Hutton and Coughtrey 1874a). Improbable as this latter point is, it has not been tested by radiocarbon dating until now. Our particular impetus to do so, however, arises in another way. It is from research, again largely within inland Otago, on Maori artefacts which have also been made from various other kinds of soft tissues (flax, grasses, dog skin, bird skin, feathers etc.). A series of accelerator mass spectrometry (AMS) radiocarbon dates on these (Anderson et al., n.d.) shows that they are exclusively late, post-AD 1650, which begs the question of why the age range does not extend across the full prehistoric period, beginning about AD 1300.

text: Radiocarbon dates on desiccated moa (Dinornithiformes) flesh from Inland Otago, New Zealand Remains of soft tissues from extinct moa (Dinornithiformes), mainly desiccated sinew, muscle, skin, and feathers have been recovered rather seldom but their distribution is distinctive. Of 22 records of such finds accumulated between 1864 and 1987, 15 came from inland Otago (Anderson 1989: 67–68, Table 5.2), west of Dunedin in the southern South Island. Most were found in the late nineteenth century (13 records) in rockshelters, clefts or alluvial sediments and were regarded at the time as evidence of the survival of moa up to about AD 1800 (Hutton and Coughtrey 1874a). Improbable as this latter point is, it has not been tested by radiocarbon dating until now. Our particular impetus to do so, however, arises in another way. It is from research, again largely within inland Otago, on Maori artefacts which have also been made from various other kinds of soft tissues (flax, grasses, dog skin, bird skin, feathers etc.). A series of accelerator mass spectrometry (AMS) radiocarbon dates on these (Anderson et al., n.d.) shows that they are exclusively late, post-AD 1650, which begs the question of why the age range does not extend across the full prehistoric period, beginning about AD 1300.

year: 2010