Model Development. Thermal transfer limits were calculated for summer peak load conditions without and with the SPS. The cases without the SPS (Case 1) and with the SPS (Case 2) are described in Section 3.4.
Model Development. Voltage transfer limits were calculated for summer peak load conditions without and with the SPS. The cases without the Project (Case 1) and with the Project (Case 2) are described in Section 3.4.
Model Development. The contingencies shown in Table 5-3 were simulated for the cases without and with the SPS.
1. Case without SPS (Benchmark) with Athens dispatched at 700 MW
2. Case with SPS with Athens dispatched at 1080 MW and a 240 MVAr capacitor bank installed at Millwood In preparing the above cases, Siemens PTI used a power flow base case provided by the NYISO, which differed somewhat from the case used in the steady state analysis. In the power flow case provided for stability analysis, Athens was dispatched at 800 MW on three combined cycle trains. For consistency with the case used in steady-state analysis, Siemens PTI reduced Athens dispatch from 800 MW to 700 MW on two combined cycle trains. The MW reduction was balanced by units in Ontario. This case is referred to as the Benchmark Case without SPS. Then, Siemens PTI developed a stability power flow case with the SPS. In this case, Athens was increased to its full capacity i.e., 1080 MW, to increase flow on the Athens-Pleasant Valley and Leeds-Pleasant Valley (Lines 91 and 92) path. The additional Athens generation was dispatched against existing units in Con Ed. For consistency with the case used in steady-state analysis, a 240 MVAR capacitor was added at Millwood.
Model Development. The consultant shall utilize the FDOT approved computer-based tools are to calculate and evaluate signal timing. Since many of these tools assume the presence of under-saturated conditions, it is important to recognize their capabilities and limitations. The requirements for developing timings for saturated and under-saturated conditions should be considered as the model is developed. The consultant should consider the following elements: • Establish a “standards and conventions” document (i.e., file naming, map settings, base data parameters, analysis settings) that provides the user with consistency through the retiming process; • Review the plan development in levels or stages to ensure efficiency; • Coordinate with the respective signal maintaining agencies; and • Include quality assurance and quality control measures.
Model Development. This section describes the methods used to compile data for the hydrologic restoration evaluation. The goal of the hydrologic report is to conduct an evaluation on pre and post hydrologic conditions that will occur during the rehydration of the Breakfast Point mitigation area. The Breakfast Point primary stormwater management system (PSWMS) consists of connected series of natural creeks and ditched canals. Characteristic data was obtained from a new field survey, site visits, interviews, topographic and aerial maps. Survey locations and data are illustrated in Exhibits 3-1 and 3-2. For this study, the existing Breakfast Point PSWMS was represented with sixteen (16) hydrologic basins (storage) connected by thirteen (13) link (conveyance/structure) nodes. A nodal diagram is included in Exhibit 3-3. The nodes identified in the Breakfast Point PSWMS can be classified as either conveyance or storage elements. Conveyance elements include closed conduits, open channels, bridge crossings and road overflows that collect and route runoff through the system. Storage elements (basin nodes) include closed basins, natural depression areas that store and attenuate runoff within the system. Sixteen basins were delineated for this study and are identified in Exhibit 3-4 and represented with the symbols N-10 through N-160. Link structures (culverts, bridges, low areas) at basin outflows are also represented in Exhibit 3-4 and are labeled similar to L-071.
Model Development. Different aspects of model development for SNNs were considered: 1) whether the hyperparameters were tuned and which was the performance criterion for model development. 2) how the prognostic variables were scaled, 3) which programming language was used. Hyperparameters are fundamental to the architecture of an XXX. They fine-tune the performance of a prediction model, preventing overfitting and providing generalizability of the model to new ”unseen” data. Choice of hyper- parameters can be a challenge in the modern era of building SNNs with state-of-the-art software that allows for numerous choices. Commonly tuned parameters were penalty terms in the likelihood (e.g., weight decay) and the number of units (nodes) in the hidden layer(s). In the majority of studies (15, 62.5%), the approach to training hyperparameters was unclear, with 6 of these studies (25.0%) failing to report whether parameters were tuned or default values were chosen. In 4 studies (16.7%) parameters were tuned, in 3 studies (12.5%) some parameters were tuned and some were assigned default values, while in 2 studies (8.3%) default values only were chosen for the hyperparameters. The performance criterion for model development (hyperparameter tuning) was examined across the 24 studies. The training criterion was unclear for 6 studies (25.0%). For 5 studies (20.8%), neural network hyperparameters were trained based on the log-likelihood, for 3 studies based on the C-index (12.5%), and for 2 studies (8.3%) based on the Area Under the Curve (AUC). Other criteria used for model development are provided in the Supplementary Material. Better reporting of the choice of hyperparameters (which parameters were selected) and of the training procedure (how they were tuned) is needed. This will help researchers to better understand how the model was developed and will facilitate reproducibility. In ANNs, input features are typically scaled to ensure that all features have a comparable scale, which allows an update of the same rate, resulting in faster algorithm convergence. The procedure was unclear in 10 of the 24 studies (41.7%), scaling was unnecessary in 7 studies (29.2%), and normalization (minimum and maximum values of features are used for scaling) was applied in 5 studies (20.8%). Standardization (mean and standard deviation of features are used for scaling) was applied in only 2 studies (8.3%). A precise description of the scaling approach (normalization or standardization) should be provide...
Model Development determine cell number and growth kinetics for use in efficacy model (do we need to create a new luciferized model)
Model Development. Post-TRIPOD, the number of studies that included predictors based on significance levels in univariable analysis decreased (pre-TRIPOD: 67%, post-TRIPOD: 44%, Figure 2 and Supplementary Table 8) as well as the number of studies using stepwise methods to retain pre- dictors (pre-TRIPOD: 63%, post-TRIPOD: 48%). In general, a larger number of candidate predictors was used in the post-TRIPOD period (median: 25), compared with pre-TRIPOD period (median: 20). Internal validation was more frequently performed in the post-TRIPOD period (74%) compared with the pre-TRIPOD period (62%). When internal validation was performed, bootstrapping was the most frequently used method in both time periods with an increase from 29% in the pre-TRIPOD period to 41% in the post-TRIPOD period. Chapter 7 The majority of studies presented measures of calibration (pre-TRIPOD: 66%, post-TRIPOD: 87%) and discrimination (pre-TRIPOD: 91%, post-TRIPOD: 100%, Figure 3 and Supple- mentary Table 7).A calibration plot and this increased in the post-TRIPOD period (pre-TRI- POD: 50%, post-TRIPOD: 82%)). Discrimination was primarily assessed with the C-statistic and Area Under the Curve (AUC) methods (pre-TRIPOD: 91%, post-TRIPOD: 100%). Measures of classification were reported in more than half of the studies (pre-TRIPOD: 69%, post-TRIPOD: 58%),mostly assessed with diagnostic test summary statistics (i.e. sensitivity, specificity and positive and negative predictive values) (pre-TRIPOD: 63%, post-TRIPOD: 50%) and to a lesser extent the integrated discrimination improvement (IDI; pre-TRIPOD: 16%, post-TIRPOD: 11%) or the net reclassification improvement (NRI; pre-TRIPOD 25%, post-TRIPOD: 18%).
Model Development. Xxxxxxxxx developed a new TransCAD-based travel demand model for the KYTC District 9 region. The model covers eight counties (Bath, Carter, Elliott, Fleming, Greenup, Xxxxx, Xxxxx and Xxxxx) in northeastern Kentucky and three counties (Xxxxx, Xxxxx and Scioto) in southern Ohio. KYTC On-Call Modeling. Since the early 2000s Xxxxxxxxx has served the Kentucky Transportation Cabinet as an on-call modeling consultant. This work has covered:
Model Development. The model is based on the following principles: • the fall in reservoir pressure over a period of time is directly related to the cumulative production over that period • the cumulative production is equal to the integrated flow rate over time • the flow rate is equal to the rate of change of reservoir pressure. Two principal sets of equations were developed. The first describes the behaviour of Xxxxx’s reservoir pressure in the Stand-Alone case: P' = (P° − P )eμt + P α α D D (1) Where, α P ’ Stand-alone Alpha reservoir pressure (at time t). (The dash ’ denotes α the Stand-Alone case and is not the differential operator) P ° Initial Alpha reservoir pressure PD Delivery pressure (at gas plant) t time μ is a constant determined by several system parameters; see Section 6 for its definition. The second describes Alpha’s reservoir pressure in the Shared Production case: Pα = C eλ1t + C eλ2t + P 1 2 D (2) Where, Pα Shared Production Alpha reservoir pressure (at time t) C1, C2, λ1 and λ2 are all constants determined by the system parameters; see Section 6 for their definition. Hence, at any point in time these equations provide the reservoir pressures for the two scenarios. Two analogous equations provide the flow rate at any point in time. Alpha’s flow rate in the Stand-Alone case is given by: Q' = μ (P° − P )eμt α k α D α (3) Where, Q ’ Stand-alone Alpha flow rate (at time t) kα Constant relating Alpha’s reservoir pressure to its gas volume And for the Shared Production case: Q = λ1 C eλ1t + λ2 C eλ2t α k 1 k 2 w w (4) Where, Qα Shared Production Alpha flow rate (at time t)
