Notice bibliographique
Résumé
Describes the forecasting process used at Columbia Gas ... accuracy of Design Day Forecasts and Daily Operational Forecasts is very critical ... both time series and regression models play an important role in forecasting. Columbia Gas of Ohio places great importance on its daily forecasting process and has spent six years developing and improving its current daily forecasting techniques. The daily demand forecasts are used in preparing strategic plans, financial projections, rate case and other regulatory proceedings, distribution system design, gas supply & capacity planning, and operational planning. Columbia's forecasting process has resulted in very small error percentages. In fact, results from a 1995 survey administered by the American Gas Association and Canadian Gas Association found Columbia's forecast accuracy to be among the best in the industry. This article discusses Columbia's forecasting process, historical accuracy of the forecast and two major types of daily forecasts developed at Columbia: Design Day Forecast and Daily Operational Forecast. Columbia Gas of Ohio (Columbia) is one of the five distribution subsidiaries of Columbia Energy Group and is the largest natural gas utility in Ohio having nearly 1.3 million customers in more than 1,000 communities. Columbia Gas of Ohio is headquartered in Columbus, Ohio. During 1999 Columbia delivered 99 BCF to Sales customers and 208 BCF to Transport customers. Columbia Energy Group, based in Herndon, Va., is one of the nation's leading energy services companies, with assets of approximately $7 billion. Its operating companies engage in virtually all phases of the natural gas business, including exploration and production, transmission, storage and distribution, as well as retail energy marketing, propane and petroleum product sales, and electric power generation. TYPES OF DAILY FORECASTS Columbia develops two types of daily forecasts: Design Day Forecast and Daily Operational Forecast. While these forecasts are used for different purposes they are developed from a single consistent forecasting process, which we will describe later. DESIGN DAY FORECAST The Design Day Forecast is primarily used to determine the amount of gas supply, transportation capacity, storage capacity and peaking contracts that Columbia needs to serve its contractually firm and human needs customers. Each year Columbia contracts for a portfolio of monthly, seasonal and annual supply contracts designed to meet the seasonal requirements and Design Day requirements for its firm customers. However, these same supply contracts must have purchasing flexibility for Columbia to respond to actual weather (warm or cold) and operating conditions. Many of the supply and capacity contracts contain a fixed cost (Demand Cost) which is paid regardless of use and a variable cost (Commodity Cost). In establishing supply and capacity levels, it is very important that the Design Day Forecast is accurate. If a company's design forecast is not accurate it may not contract for the proper supply and capacity assets. This could place the company's customers at economic or service risk. Over the past seven years Columbia's Mean Absolute Percent Error (MAPE) of the Design Day Forecast has averaged 0.4%. Table 1 shows the annual MAPE for each of the past seven Design Day Forecasts. The Design Day Forecast is based on Columbia's Design Conditions, which consist of the following: Design Current Day Temperature, Design Prior Day Temperature, and Design Wind Speed. DESIGN CONDITIONS Both Design Temperatures are developed based upon the analysis of all available historical weather data going back to 1949. Traditionally, Columbia updates this historical temperature data for analytical purposes approximately every five years. Table 2 shows the Design Temperature and Design Wind Speed for the 4 major geographical areas served by Columbia. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».