Fuel-Switching Space Heating System Performance and Residential Peak Load Characterization and Estimation
Notice bibliographique
Résumé
In the residential sector, energy consumption and greenhouse gas (GHG) emissions remain key issues. Heat pumps are an emerging technology that shows promise at addressing both these issues for space heating, which has been traditionally met through fossil fuels. Cold climates pose a unique challenge to the successful integration of heat pumps due to reduced efficiency in lower ambient temperatures. Fuel-switching heating systems are a solution that can be installed as new or retrofitted systems, potentially reducing utility costs, GHG emissions, or energy consumption through the optimized utilization of multiple fuel sources. Natural gas and power data were collected and used throughout this study from six occupied townhome units in Edmonton, Alberta, each equipped with fuel-switching systems. The key lessons learned from an energy and emissions analysis of the in-situ study were (1) The studied units had lower energy consumption but higher GHG emissions compared to Canadian average rowhouse data, showing caution is necessary when implementing electrification measures in regions with high emission intensities, and (2) The overall efficiency of fuel-switching heating systems is dependent on operation strategies, where this study uncovered poor utilization of the fuel-switching heating system components. Another important aspect of residential energy consumption is the peak load, which requires the highest utilization of the grid’s generation resources for short durations. To meet these loads, additional flexible generator plants are used which typically have higher operational costs and GHG emissions. It’s beneficial for decision-makers, designers, and distributors to have information and estimates regarding future peak loads. Therefore, residential peak load was characterized through parametric PDF fitting for six single-mode PDFs and several Gaussian mixture distributions (GMDs). The goodness-of-fit for each distribution was evaluated using quantile-quantile plots (QQ-Plots) and the 2-sample Kolmogorov-Smirnov test. The results show that GMDs had the best performance for characterising peak load distributions for single households, with successful fits achieved for 79.6% and 66.7% of hourly and minutely resolution peak load datasets, respectively. From the tested single-mode PDFs, the Generalized Extreme Value (GEV) distribution had the best performance, successfully fitting 50%, 11%, and 33% of the tested total, HVAC, and plug hourly peak load datasets. Higher-resolution minutely peak load data was found to have increased distribution complexity compared to low-resolution data, requiring more complex PDFs for successful characterization. For estimating future peak load capacity, an aggregated peak load estimation method was developed based on a sampling-based convolution approximation modified to incorporate factors that drive common peak loads between households including time of day and exterior temperature. Sample demand data was generated individually for each household consisting of datasets for HVAC, plug, and total demand for three years, with sampled trials summed to form a building-level aggregated peak load distribution. From the distribution, the 99th percentile corresponded to 30,284 W, the threshold level for a 1% likelihood of exceedance over three years. From this estimate, the nominal peak capacity of the installed electrical transformer could be downsized by 29,716 W (49.53%) with a 99% likelihood of meeting all demands.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».