Wind Speed Prediction Models and Their Use in Wind Turbine Control

Wind energy production is a section that became bigger and bigger thanks to the interests in finding new ways to produce energy that do not involve fossil fuels because of environment concerns, because of their costs and because of their limited amount. Even though wind energy was exploited also in the past, for example in wind mills, it is with wind turbines that all the problems affecting these systems started to be taken into account and to be studied deeply. Rising sizes, flexible materials used, aerodynamics, unfriendly environments, wind variability are some of the challenges to face in order to improve efficiency and extend the life and reliability of these systems. It is in this wide and various context that wind prediction models are needed to understand and to know in advance, in a wind farm, how wind will change and which effects it will have on a turbine and the following ones. In this work an approach to forecast the wind at downwind positions, using upwind turbines as sensors, is proposed. The work is based on real data from Thanet Wind Farm provided by Vestas Wind Systems A/S. The models found are relevant only for the sequences taken into account but they show the possibility to exploit existing turbines as wind sensors and open the way for further development of this work. This argument appear to be (to my knowledge) new, since in literature almost no references were found

Effect of a biological additive on nitrogen losses from pig slurry during storage

Additives applied to animal manure slurries can affect the chemical composition and the biological processes of slurries during storage, with possible improvement of their management and reduction of environmental problems. Some new formulations are marketed claiming a nitrogen (N) removal effect due to denitrification, with the consequence of a reduced N content in the manure after storage. This study evaluated the effects of one of these commercial additives (BACTYcomplex) on slurry characteristics and N losses at a commercial piggery. The additive was applied to four different sectors of the piggery, each with an independent under-floor slurry pit; four other sectors served as controls without treatment. Pits were emptied every 4 wk, and the manure was analyzed for total and ammonia-N and total and volatile solids. Slurry samples from the last month of the on-farm assessment were removed and stored thermostatically in vessels external to the piggery. A subsample of slurry that was treated with the additive at the piggery was treated with an additional dose of additive at the beginning of long-term storage. The additive did not change the composition of the slurry during in-house storage (4 wk duration). During the 155 d of external thermostatic storage, the total solids content of treated slurry was reduced by 18% compared with control slurry, but the N content and composition of treated slurry was unaffected. The additive had a positive effect in accelerating the stabilization of the slurry but did not modify N losses

A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy

Due to its increasing pressure on dairy cows, studies that investigate how to cope with heat stress are needed. The heat stress affects multiple aspects of cows' lives, among which their behavior and welfare. In this study, a survey was carried out in eight farms located in Northern Italy to monitor and evaluate the environmental aspects of the barns and the behavioral responses of dairy cows. For one year, three periods were monitored: thermoneutral (T_S), hot (H_S) and cold (C_S) seasons. Temperature and relative humidity were measured by environmental sensors, and lying vs. standing time, number of lying bouts and their average duration were collected by accelerometers. The temperature-humidity index (THI) was quantified inside and outside of the barn. Results show that at the increase of the THI, behavioral adaptations occurred in all the farms, especially with a reduction of lying time and an increase of respiration rate. Four of the eight farms need interventions for improving the cows' welfare. Here, environmental problems should be solved by introducing or improving the efficacy of the forced ventilation or by modifying the barn structure. Monitoring dairy barns with sensors and Precision Livestock Farming techniques can be helpful for future livestock farming to alert farmers on the need for their interventions to respond immediately to unwanted barn living conditions

Technical, economic, and environmental assessment of a collective integrated treatment system for energy recovery and nutrient removal from livestock manure

The aim of this 5-year study was to evaluate the technical, economic, and environmental performances of a collective-based integrated treatment system for bioenergy production and nutrients removal to improve the utilization efficiency and reduce the environmental impact of land applied livestock manure. The study involved 12 livestock production units located in an intensive livestock area designated as nitrate vulnerable zone with large N surplus. The treatment system consisted of an anaerobic digestion unit, a solid-liquid separation system, and a biological N removal process. Atmospheric emissions and nutrient losses in water and soil were examined for the environmental assessment, while estimated crop removal and nutrient utilization efficiencies were used for the agronomic assessment. The integrated treatment system achieved 49% removal efficiency for total solids (TS), 40% for total Kjeldahl nitrogen (TKN), and 41% for total phosphorous (TP). A surplus of 58kWh/t of treated manure was achieved considering the electricity produced by the biogas plant and consumed by the treatment plant and during transportation of raw and treated manure. A profit of 1.61 \ubf/t manure treated and an average reduction of global warming potential by 70% was also achieved. The acidification potential was reduced by almost 50%. The agronomic use of treated manure eliminated the TKN surplus and reduced the TP surplus by 94%. This collective integrated treatment system can be an environmentally and economically sustainable solution for farms to reduce N surplus in intensive livestock production areas

Influence of feed delivery frequency on behavioural activity of dairy cows in freestall barns

Research on feeding management in more competitive free-stall settings indicates that frequency of delivery of fresh feed stimulates feed bunk attendance and can affect other aspects of cows’ time budgets apart from feeding such as time spent standing vs. lying down. The objective of this study was to examine how the frequency of feed delivery affects the behavior in two farms, one with a conventional and one with automatic milking system (AMS). The feeding frequency was varied from two to three times per day in the conventional dairy farm; one to two times per day in the AMS farm. The experiment was carried out in two different seasons. All behaviours of the cows were monitored in continuous by video recording. As expected, behavioral indices have been significantly affected by environmental conditions both in conventional farm and AMS farm. The variation in the frequency of feed delivery seems to affect the cow behavioural activity only in a limited way and modify only slightly the daily averages of the time spent in different activities mainly increasing the time cows spend standing (+4- 5%)

Effects of climatic conditions on the lying behavior of a group of primiparous dairy cows

Currently, lying behavior can be assessed using continuous observations from sensors (e.g., accelerometers). The analysis of digital data deriving from accelerometers is an effective tool for studying livestock behaviors. Despite the large interest in the lying behavior of dairy cows, no reference was found in literature regarding the prediction of lying behavior as a function of the interaction of environmental parameters. The present study aimed to evaluate the influence of climatic conditions (temperature-humidity index, solar radiation, air velocity and rainfalls) on the lying behavior of a group of primiparous dairy cows, using data from accelerometers, and develop a prediction model to identify and predict the lying behavior of dairy cows as a function of the effects of environmental conditions. Results from the. GLM Procedure (SAS) showed that the model was highly significant (p < 0.001) and the r2 was 0.84. All of the effects in the model resulted in being highly significant (p < 0.001). This model, if validated properly, could be a valid early warning system to identify any deviation from the expected behavior, and to assess the effectiveness of thermal stress mitigation strategies

Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (ly-. ing bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine -learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sen-sitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential

Relationships among lying and standing behaviour, body condition score and milk production in primiparous cows

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Nutrient losses during winter and summer storage of separated and unseparated digested cattle slurry

Management factors affect nutrient loss during animal manure slurry storage in different ways. We conducted a pilot-scale study to evaluate carbon (C) and nitrogen (N) losses from unseparated and digested dairy slurry during winter and summer storage. In addition to season, treatments included mechanical separation of digestate into liquid and solid fractions and bimonthly mixing. Chemical analyses were performed every 2 wk for the mixed materials and at the start and end of storage for unmixed materials. The parameters examined allowed us to estimate C and N losses and examine the factors that determine these losses as well as emission patterns. Gas measurements were done every 2 wk to determine the main forms in which gaseous losses occurred. To evaluate the effect of separation, measured losses and emissions of separated liquid and solid fractions were mathematically combined using the mass separation efficiency of the mechanical separator. Nutrient losses were mainly affected by climatic conditions. Losses of C (up to 23%) from unseparated, unmixed digestate and of N (38% from combined separated fractions and from unseparated digestate) were much greater in summer than in winter, when C and N losses were < 7%. Mixing tended to significantly increase N losses (P < 0.1) only in winter. Mechanical separation resulted in lower GHG emissions from combined separated fractions than from unseparated digestate. Results indicate that to maximize the fertilizer value of digested slurry, dairy farmers must carefully choose management practices, especially in summer. For separated digestates, practices should focus on storage of the liquid fraction, the major contributor of C and N losses (up to 64 and 90% of total losses, respectively) in summer. Moreover, management practices should limit NH3, the main form of N losses (up to 99.5%)