Assessing the sensitivity of Canadian hydro and wind power production potentials to climate variability and change
Notice bibliographique
Résumé
Increasing population, limiting fossil fuels, along with looming effects of climate change and environmental degradation have forced the energy industry to look for alternative energy sources, most particularly from renewable resources, including wind, solar and hydro. Among these, hydropower is the dominant renewable energy source in Canada in terms of production, with enormous potential for further growths due to large water availability. Wind power also constitutes the fastest growing renewable energy source in Canada during the recent past. Both hydro and wind power productions are largely dependent on local and regional climate conditions. As a result, climate variability and change can greatly affect their availability in time and space. This study aims at providing a first-hand analyses of the sensitivity of hydropower and wind energy to climate variability and change across Canadian regions using a suite of statistical techniques and inference approaches. More specifically for hydropower production, trends in effective climate variables along with the dependencies and causalities of these variables with monthly hydropower production is assessed. These analyses lead to the development of a set of predictive statistical models, with which the expected future monthly hydropower production can be projected in light of the existing trends in effective climate variables across Canadian political jurisdiction. For wind energy, the dependency between temperature and wind speed is explored at the local scale with a greater goal of understanding how gradual changes in temperature during the recent past has resulted into trends in wind speed across different time scales and/or Canadian regions. Our results show that Canada has become warmer, slightly wetter with more contribution from rain than snow, and less windy. In addition, provincial monthly hydropower productions demonstrate strong dependence with effective climate variables, however the sign and magnitude of such dependencies are subject to spatial and temporal differences. Our results show that depending on the province, climate variables in a given time step can cause changes in the hydropower production between up to 20 months ahead. The sensitivity analyses made by developed predictive models also show that continuation of the existing trends in climate variables can cause some changes in the expected annual pattern of hydropower production across Canadian providences and territories, which are more intense during the warmer seasons. Although net effect of climate change over the entire Canadian landmass is suggested to be positive, there are significant seasonal and regional losses in hydropower production, for instance in Alberta and British Columbia. With respect to the wind speed, negative trends in wind speed were found for most of the stations throughout the country. Although during the same period, positive trends are also observed in temperature, it is shown that in majority of cases there is no significant dependency between local temperature and wind speed; yet, it should be noted that this, to some extent, is governed by the threshold used to account for the significance of the dependency. Considering stations that are shown significant dependency between the local temperature and wind speed, the negative influences of increasing local temperature on the local wind speed are quantified across northern, western, eastern and Atlantic Canada. Our results provides a fresh look at the future of hydro and wind energy productions in Canada under climate variability and change, which have enormous implications to natural resource management. The statistical frameworks developed due to the \ncourse of this thesis can be used in other parts of the globe, where data support is available, to address the sensitivity of hydropower and wind energy to climate variability and change.
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 | 0,005 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».