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Academic literature on the topic 'Energy conservation – Ontario – Forecasting'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Energy conservation – Ontario – Forecasting"

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Rodriguez, C. P., and G. J. Anders. "Energy Price Forecasting in the Ontario Competitive Power System Market." IEEE Transactions on Power Systems 19, no. 1 (February 2004): 366–74. http://dx.doi.org/10.1109/tpwrs.2003.821470.

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Farmer, David J., and Layne N. Thiessen. "Recent Regulatory and Legislative Developments of Interest to Energy Lawyers." Alberta Law Review 51, no. 2 (December 1, 2013): 427. http://dx.doi.org/10.29173/alr73.

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This article highlights important legislative and regulatory developments of relevance to energy lawyers, including those involving electricity matters and related jurisprudence that arose between May 2012 and May 2013. The authors have reviewed a wide variety of subject areas, including examining decisions of key regulatory agencies such as the National Energy Board, the Canadian Environmental Assessment Agency, Alberta’s Energy Resources Conservation Board, the Alberta Utilities Commission, the Alberta Surface Rights Board, the Ontario Energy Board, the Ontario Environmental Review Tribunal, and the World Trade Organization. Additionally, federal and provincial legislation and regulations of significance introduced during this period are canvassed.
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López, Karol Lina, Christian Gagné, Germán Castellanos-Dominguez, and Mauricio Orozco-Alzate. "Training subset selection in Hourly Ontario Energy Price forecasting using time series clustering-based stratification." Neurocomputing 156 (May 2015): 268–79. http://dx.doi.org/10.1016/j.neucom.2014.12.052.

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Xie, Hui, Li Feng Wang, and Wei Liang. "Forecasting of Energy Consumption of Beijing's Residential Sector Based on System Dynamics Model." Advanced Materials Research 869-870 (December 2013): 537–40. http://dx.doi.org/10.4028/www.scientific.net/amr.869-870.537.

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Beijing is a major municipality/province of energy consumption, but poor in energy resources. The inherent and complete dependence on importing energy makes energy security extremely difficult, which draws more attention to the energy conservation in Beijing. With the improvement of people's living standard, the proportion of the residential energy consumption continuously increased. Residential energy saving became the key field of energy conservation and environmental protection. A great many factors of which the relations are complex affect the energy conservation. By introducing System Dynamics analysis, which has a unique advantage of analyzing the multiple and complex feedback system, this paper aims to analyze energy consumption of Beijings residential sector and finally comes to some suggestions towards governments policies.
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Gao, Lei. "Forecasting and Analysis of Energy Consumption in China." Frontiers in Business, Economics and Management 3, no. 2 (March 16, 2022): 26–30. http://dx.doi.org/10.54097/fbem.v3i2.257.

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Energy is essential to the development of an economy and society. In recent years, China's rapid economic development has created the "China Miracle", but it has also led to a sharp increase in energy consumption in China. To ensure the achievement of the ambitious goal of reaching the carbon peak by 2030, it is of great significance to study the total energy consumption in China in order to promote the national energy conservation and emission reduction actions. This paper constructs models GM(1,1), DGM(1,1), and gray Verhulst model based on the original data of China's total energy consumption from 2001 to 2020, and constructs a combined forecasting model by the least squares method to make an economic forecast of China's energy consumption in the next five years. It provides a theoretical basis for making a reasonable energy planning.
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Deng, Zhuofu, Xianglong Qi, Tengteng Xu, and Yingnan Zheng. "Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting." Computational Intelligence and Neuroscience 2022 (February 21, 2022): 1–18. http://dx.doi.org/10.1155/2022/9326856.

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In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour meter, accurate price prediction becomes more and more crucial in the energy management and control of energy storage systems. Due to the great uncertainty of electricity price, the performance of the general electricity price forecasting models is not satisfactory to be adopted in practice. Therefore, in this paper, we propose a novel electricity price forecasting strategy applied in optimization for the scheduling of battery energy storage systems. At first, multiple nonstationary decompositions are presented to extract the most significant components in price series, which express remarkably discriminative features in price fluctuation for regression prediction. In addition, all extracted components are delivered to a devised deep convolution neural network with multiscale dilated kernels for multistep price forecasting. At last, more advanced price fluctuation detection serves the optimized operation of the battery energy storage system within Ontario grid-connected microgrids. Sufficient ablation studies showed that our proposed price forecasting strategy provides predominant performances compared with the state-of-the-art methods and implies a promising prospect in economic benefits of battery energy storage systems.
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Runge, Jason, and Radu Zmeureanu. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review." Energies 12, no. 17 (August 23, 2019): 3254. http://dx.doi.org/10.3390/en12173254.

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During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.
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Larroque, Jeremy, Julian Wittische, and Patrick M. A. James. "Quantifying and predicting population connectivity of an outbreaking forest insect pest." Landscape Ecology 37, no. 3 (December 23, 2021): 763–78. http://dx.doi.org/10.1007/s10980-021-01382-9.

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Abstract Context Dispersal has a key role in the population dynamics of outbreaking species such as the spruce budworm (Choristoneura fumiferana) as it can synchronize the demography of distant populations and favor the transition from endemic to epidemic states. However, we know very little about how landscape structure influences dispersal in such systems while such knowledge is essential for better forecasting of spatially synchronous population dynamics and to guide management strategies. Objectives We aimed to characterize the spatial environmental determinants of spruce budworm dispersal to determine how these features affect outbreak spread in Quebec (Canada). We then apply our findings to predict expected future landscape connectivity and explore its potential consequences on future outbreaks. Methods We used a machine-learning landscape genetics approach on 447 larvae covering most of the outbreak area and genotyped at 3562 SNP loci to identify the main variables affecting connectivity. Results We found that the connectivity between outbreak populations was driven by the combination of precipitation and host cover. Our forecasting suggests that between the current and next outbreaks, connectivity may increase between Ontario and Quebec, and might decrease in the eastern part, which could have the effect of limiting outbreak spread from Ontario and Quebec to the eastern provinces. Conclusions Although we did not identify any discrete barriers, low connectivity areas might constrain dispersal in the current and future outbreaks and should in turn, be intensively monitored. However, continued sampling as the outbreak progresses is needed to confirm the temporal stability of the observed patterns.
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Smith, L. E., Marie H. Buchinski, and And Deirdre A. Sheehan. "Recent Regulatory and Legislative Developments of Interest to Energy Lawyers." Alberta Law Review 48, no. 2 (December 1, 2010): 417. http://dx.doi.org/10.29173/alr160.

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This article identifies recent regulatory and legislative developments of interest to oil and gas lawyers. The authors survey a variety of subject areas, examining decisions of key regulatory agencies such as the National Energy Board, the Ontario Energy Board, the Alberta Energy Resources Conservation Board, the Alberta Surface Rights Board, and the Alberta Utilities Commission, as well as related court decisions. In addition, the authors review a variety of key policy and legislative changes from the federal and provincial levels.
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Wei, Shangfu, and Xiaoqing Bai. "Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network." Energies 15, no. 5 (February 25, 2022): 1743. http://dx.doi.org/10.3390/en15051743.

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Short-term building energy consumption forecasting is vital for energy conservation and emission reduction. However, it is challenging to achieve accurate short-term forecasting of building energy consumption due to its nonlinear and non-stationary characteristics. This paper proposes a novel hybrid short-term building energy consumption forecasting model, SSA-CNNBiGRU, which is the integration of SSA (singular spectrum analysis), a CNN (convolutional neural network), and a BiGRU (bidirectional gated recurrent unit) neural network. In the proposed SSA-CNNBiGRU model, SSA is used to decompose trend and periodic components from the original building energy consumption data to reconstruct subsequences, the CNN is used to extract deep characteristic information from each subsequence, and the BiGRU network is used to model the dynamic features extracted by the CNN for time series forecasting. The subsequence forecasting results are superimposed to obtain the predicted building energy consumption results. Real-world electricity and natural gas consumption datasets of office buildings in the UK were studied, and the multi-step ahead forecasting was carried out under three different scenarios. The simulation results indicate that the proposed model can improve building energy consumption forecasting accuracy and stability.
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Dissertations / Theses on the topic "Energy conservation – Ontario – Forecasting"

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Dodds, Gordon Ivan. "Modelling and forecasting electricity demand using aggregate and disaggregate data." Thesis, Queen's University Belfast, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306073.

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McCafferty, Peter. "Forecasting electricity demand in the industrial sector based on disaggregate data." Thesis, Queen's University Belfast, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385049.

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Hadjipaschalis, Constantinos. "An investigation of artificial neural networks applied to monthly electricity peak demand and energy forecasting." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286627.

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Motsomi, Abel Pholo. "Statistical tools for consolidation of energy demand forecasts." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1011578.

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The electricity market in the South African economy uses specialised instruments in forecast-ing the energy load to be delivered. The current status quo operates with several forecasters from different offices, departments or businesses predicting for different purposes. This be-comes a challenge to derive a consolidated forecast. This study has attempted to develop a consolidating instrument that will merge all the forecasts from different offices, departments or businesses into one so-called ‘official forecast’. Such an instrument should be able to predict with accuracy the anticipated usage or demand. Article [18] examined patterns across G7 countries and forecasters to establish whether the present bias reflects the inefficient use of information, or whether it reflects a rational re- sponse to financial, reputation and other incentives operating for forecasters. This bias is particularly true for any electricity utility as forecasting is undertaken by different divisions; therefore each division has its own incentives. For instance, the generation division will tend to overstate their forecasts so as that there is no possibility of a shortage, whereas distri- bution (sales) might understate so as to give the impression of being profitable when more units are sold to consumers. Thus, the study attempts to rectify this bias by employing statistical tools in consolidating these forecasts. The results presented in this paper propose a newly developed procedure of consolidating energy demand forecasts from different users and accounting for different time horizons. Predicting for the short-term and long-term involves different measuring tools, which is one aspect of prediction this paper tackles.
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Medjiah, Samir. "Optimisation des protocoles de routage dans les réseaux multi-sauts sans fil à contraintes." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14663/document.

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Durant ces dernières années, de nombreux travaux de recherches ont été menés dans le domaine des réseaux multi-sauts sans fil à contraintes (MWNs: Multihop Wireless Networks). Grâce à l'évolution de la technologie des systèmes mico-electro-méchaniques (MEMS) et, depuis peu, les nanotechnologies, les MWNs sont une solution de choix pour une variété de problèmes. Le principal avantage de ces réseaux est leur faible coût de production qui permet de développer des applications ayant un unique cycle de vie. Cependant, si le coût de fabrication des nœuds constituant ce type de réseaux est assez faible, ces nœuds sont aussi limités en capacité en termes de: rayon de transmission radio, bande passante, puissance de calcul, mémoire, énergie, etc. Ainsi, les applications qui visent l'utilisation des MWNs doivent être conçues avec une grande précaution, et plus spécialement la conception de la fonction de routage, vu que les communications radio constituent la tâche la plus consommatrice d'énergie.Le but de cette thèse est d'analyser les différents défis et contraintes qui régissent la conception d'applications utilisant les MWNs. Ces contraintes se répartissent tout le long de la pile protocolaire. On trouve au niveau application des contraintes comme: la qualité de service, la tolérance aux pannes, le modèle de livraison de données au niveau application, etc. Au niveau réseau, on peut citer les problèmes de la dynamicité de la topologie réseau, la présence de trous, la mobilité, etc. Nos contributions dans cette thèse sont centrées sur l'optimisation de la fonction de routage en considérant les besoins de l'application et les contraintes du réseau. Premièrement, nous avons proposé un protocole de routage multi-chemin "en ligne" pour les applications orientées QoS utilisant des réseaux de capteurs multimédia. Ce protocole repose sur la construction de multiples chemins durant la transmission des paquets vers leur destination, c'est-à-dire sans découverte et construction des routes préalables. En permettant des transmissions parallèles, ce protocole améliore la transmission de bout-en-bout en maximisant la bande passante du chemin agrégé et en minimisant les délais. Ainsi, il permet de répondre aux exigences des applications orientées QoS.Deuxièmement, nous avons traité le problème du routage dans les réseaux mobiles tolérants aux délais. Nous avons commencé par étudier la connectivité intermittente entre les différents et nous avons extrait un modèle pour les contacts dans le but pouvoir prédire les future contacts entre les nœuds. En se basant sur ce modèle, nous avons proposé un protocole de routage, qui met à profit la position géographique des nœuds, leurs trajectoires, et la prédiction des futurs contacts dans le but d'améliorer les décisions de routage. Le protocole proposé permet la réduction des délais de bout-en-bout tout en utilisant d'une manière efficace les ressources limitées des nœuds que ce soit en termes de mémoire (pour le stockage des messages dans les files d'attentes) ou la puissance de calcul (pour l'exécution de l'algorithme de prédiction).Finalement, nous avons proposé un mécanisme de contrôle de la topologie avec un algorithme de routage des paquets pour les applications orientés évènement et qui utilisent des réseaux de capteurs sans fil statiques. Le contrôle de la topologie est réalisé à travers l'utilisation d'un algorithme distribué pour l'ordonnancement du cycle de service (sleep/awake). Les paramètres de l'algorithme proposé peuvent être réglés et ajustés en fonction de la taille du voisinage actif désiré (le nombre moyen de voisin actifs pour chaque nœud). Le mécanisme proposé assure un compromis entre le délai pour la notification d'un événement et la consommation d'énergie globale dans le réseau
Great research efforts have been carried out in the field of challenged multihop wireless networks (MWNs). Thanks to the evolution of the Micro-Electro-Mechanical Systems (MEMS) technology and nanotechnologies, multihop wireless networks have been the solution of choice for a plethora of problems. The main advantage of these networks is their low manufacturing cost that permits one-time application lifecycle. However, if nodes are low-costly to produce, they are also less capable in terms of radio range, bandwidth, processing power, memory, energy, etc. Thus, applications need to be carefully designed and especially the routing task because radio communication is the most energy-consuming functionality and energy is the main issue for challenged multihop wireless networks.The aim of this thesis is to analyse the different challenges that govern the design of challenged multihop wireless networks such as applications challenges in terms of quality of service (QoS), fault-tolerance, data delivery model, etc., but also networking challenges in terms of dynamic network topology, topology voids, etc. Our contributions in this thesis focus on the optimization of routing under different application requirements and network constraints. First, we propose an online multipath routing protocol for QoS-based applications using wireless multimedia sensor networks. The proposed protocol relies on the construction of multiple paths while transmitting data packets to their destination, i.e. without prior topology discovery and path establishment. This protocol achieves parallel transmissions and enhances the end-to-end transmission by maximizing path bandwidth and minimizing the delays, and thus meets the requirements of QoS-based applications. Second, we tackle the problem of routing in mobile delay-tolerant networks by studying the intermittent connectivity of nodes and deriving a contact model in order to forecast future nodes' contacts. Based upon this contact model, we propose a routing protocol that makes use of nodes' locations, nodes' trajectories, and inter-node contact prediction in order to perform forwarding decisions. The proposed routing protocol achieves low end-to-end delays while using efficiently constrained nodes' resources in terms of memory (packet queue occupancy) and processing power (forecasting algorithm). Finally, we present a topology control mechanism along a packet forwarding algorithm for event-driven applications using stationary wireless sensor networks. Topology control is achieved by using a distributed duty-cycle scheduling algorithm. Algorithm parameters can be tuned according to the desired node's awake neighbourhood size. The proposed topology control mechanism ensures trade-off between event-reporting delay and energy consumption
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MacPherson, Jerry R. "Long-term energy demand forecasting : determination of an energy baseline and energy conservation supply curves for the Manitoba Health Care subsector." 1989. http://hdl.handle.net/1993/22867.

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Schembri, Jeremy. "The Influence of Home Energy Management Systems on the Behaviours of Residential Electricity Consumers: An Ontario, Canada Case Study." Thesis, 2008. http://hdl.handle.net/10012/4029.

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The current state of Ontario’s electricity system and natural environment has prompted the provincial government to call for the province to adopt a ‘culture of conservation.’ Answering this call will involve the promotion of a variety of solutions. Included in that will be the use of information and communication technology, which encompasses technologies such as home energy management system (HEMS). It is believed that the feedback and home automation features of the HEMS will enable its users to alter their electricity consumption behaviours, via net reductions and/or load shifting. This study has assessed the ability of HEMS to encourage reduction in total and on-peak electricity consumption while in a time-of-use pricing environment. Additional focus was on which consumers had the greatest success using the HEMS to adopt electricity conservation behaviours. Two hundred and sixteen participants of a Milton, Ontario HEMS pilot study were chosen to take part in this case study. These participants were divided into two equal groups: a sample group, those who received a HEMS, and a control group, those who did not receive a HEMS. Participants from both groups were asked to complete two surveys and allow their electricity consumption data to be analyzed. The initial survey was to establish some baseline information about the participants. The second survey was designed to determine if changes had occurred in the household since the initial baseline survey. Through the analysis of the survey and households electricity consumption data, conclusions were drawn on how participants used the HEMS. The study had a 2.9% relative reduction in total electricity consumption and a 13.2% relative reduction in on-peak electricity consumption. However, additional analysis of the results revealed promising findings with regard to the HEMS ability to catalyze conservation and demand management among recent time-of-use pricing adopters.
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Deline, Mary Elizabeth. "Keeping Up With the Joneses: Electricity Consumption, Publicity and Social Network Influence in Milton, Ontario." Thesis, 2010. http://hdl.handle.net/10012/5219.

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Abstract This study used an exploratory research focus to investigate if making electricity consumption public and subject to social norms and networks resulted in consumption decreases for households in Milton, Ontario. In the first phase, Milton Hydro identified customers who fell within an average annual electricity consumption category and these customers were invited to participate by mail. Due to lack of participant uptake, cold-calling, targeting of service and faith groups and commuters, and snowball sampling were employed to obtain a total participant size of 17. The second phase saw participants grouped according to social network type (occupational, faith group, etc) and exposed to approval or disapproval indicators within their group about their daily electricity consumption rates via an on-line ‘energy pool’. There were five main groups: one of neighbours, one of members of a faith group, one of members of a company, one of strangers and one of a control group. Group members saw other members’ indicators with the exception of the control group, whose indicators were privately delivered. All group’s electricity consumption was tracked through daily smart meter readings. Participants also had the option of commenting on each other’s electricity use via an online ‘comment box’. In the third phase participants were asked to participate in a questionnaire to assess: 1) the perceived efficacy of the intervention; 2) perceptions of electricity consumption; and 3) the influence of the group on these perceptions. This sequential methodology was chosen for its ability to “...explain significant (or non-significant) results, outlier results, or surprising results” (Cresswell, 2006, p. 72). The findings of this exploratory research seem to suggest the following: 1) that publicity or group type does not seem to affect electricity consumption in comparative electricity consumption feedback for this study; 2) that participants used injunctive norms to comment on their electricity consumption but directed these comments solely at themselves; and 3) that the stronger the relationships in the group, the more likely participants were to engage with the website through checking it and commenting on it. This study may be useful to those in the fields of: 1) electricity conservation who wish to leverage feedback technologies; 2) social networks who wish to better understand how tie strength interacts with social norms and; 3) those in social marketing who wish to develop norm-based campaigns.
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Simmons, Sarah Ivy. "Investigating the impacts of time-of-use electricity rates on lower-income and senior-headed households: A case study of Milton, Ontario (Canada)." Thesis, 2010. http://hdl.handle.net/10012/5041.

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Through the Smart Metering Initiative in the Canadian province of Ontario, all residential electricity customers will be converted from a tiered rate regime to a time-of-use (TOU) rate regime by the year 2010. Although TOU rates are designed to be cost-neutral for the average consumer, research suggests that TOU rates may affect consumers differently depending on their socioeconomic characteristics. In an effort to better understand the effects of TOU rates on lower-income and senior-headed households, a case-study in Milton was conducted between June and December of 2007. The overarching thesis question is: What are the behavioural responses to, and financial impacts of, TOU electricity rates on lower-income and senior-headed households? Nine expert interviews were conducted with Ontario professionals working in government, environmental non-profit groups, citizen advocacy organizations and affordable housing associations in order to provide context for the study. Time-differentiated electricity consumption data were then collected from 199 households from two senior housing complexes and two affordable housing complexes in Milton, Ontario between June and December 2007. A questionnaire was also sent to each household to determine some socio-economic and structural characteristics of the households. The electricity consumption data collected from the four sites suggest that the households would not benefit financially from TOU rates given electricity consumption behaviour during the period prior to the implementation of TOU rates in June 2007. Thus, they would have to change their behaviour in order to benefit financially from TOU rates. During this pre-TOU period, Site A, Site B and Site C would have paid more, on average, for their electricity under TOU rates than on tiered rates ($0.34, $0.61 and $0.15 per week, respectively). While Site D, on average, would have seen no change under TOU rates. A conservation effect was detected by comparing the electricity consumption from billing periods in 2006 to corresponding billing periods in 2007 after the implementation of TOU rates. Site A saw a conservation effect during the first corresponding billing period (35%); while Site B saw a conservation effect for three corresponding billing periods (21%, 24% and 9%). Site C saw a conservation effect for the first five corresponding billing periods (ranging from 8% to 21%), while Site D saw a conservation effect for all corresponding billing periods (ranging from 10% to 34%). The presence of a conservation effect at Site D was unexpected, particularly because households at Site D are not responsible for paying their own electricity bills. Although a conservation effect was observed after the implementation of TOU rates, the extent to which it could be attributed to the implementation of TOU rates is unclear, and should be investigated further. There was no considerable shift in the proportion of electricity consumed during each of the peak periods during the summer TOU period for Site A and Site D after the introduction of TOU rates. There was, however, a slight reduction in the portion of electricity consumed during the summer TOU period for Site B and Site C (0.2% and 0.1% per week, respectively). Due to the change in the on-, mid- and off-peak schedule from the summer TOU period to the winter TOU period, the households consume more electricity during the off-peak periods in the winter than they do during the off-peak periods in the summer (even though their patterns of consumption do not change). Similar to the pre-TOU period, during the summer post-TOU period, Site A and Site B, and Site C, on average, paid more for electricity (commodity) under TOU rates than they would have paid if they had continued on tiered rates ($0.38, $0.51 and $0.16 more per week, respectively), while Site D would have seen no change in their electricity costs. In contrast, during the winter post-TOU period several sites paid less for electricity on TOU rates than they would have if they had continued on tiered rates. Site B, Site C and Site D paid, on average, $0.78, $0.16 and $1.76 less per week, respectively. Although Site A paid more under on TOU rates during the winter post-TOU (on average $0.18 more per week), the cost was less than during the summer post-TOU period. The change in costs expressed here does not reflect any reduced costs that may have resulted from conservation. For example, if the households were shown to have a conservation effect, they might have lower electricity costs. Additionally, the changes in costs do not reflect any additional fees or charges that might be attributed to the smart meter installation and the Smart Metering Initiative (e.g., additional fees from Milton Hydro). In conclusion, TOU rates appear to be ineffective at motivating these lower-income and senior-headed households in Milton, Ontario to shift electricity from on-peak periods to off-peak periods, however, a reduction in electricity usage may be attributed to TOU rates. Further research is required to confirm these effects. It is important to note that some of the lower-income and senior-headed households in this study appeared to see an increase in their electricity bill, particularly during the summer TOU period. Lower-income and senior-headed households are thought to be less able to shift electricity consumption, therefore it is important to develop mechanisms to identify households that are at risk of bill increases.
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Books on the topic "Energy conservation – Ontario – Forecasting"

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Canada. Library of Parliament. Science and Technology Division. and Canada. Library of Parliament. Research Branch., eds. Energy efficiency: Future improvement. Ottawa: Research Branch, Library of Parliament, 1992.

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An energy efficient Ontario: Toward the year 2000. [Toronto]: The Ministry, 1986.

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Organization for Economic Co-operation and Development. World energy outlook. Paris: OECD, 1996.

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Energy, Ontario Ministry of. Energy 2000: An Energy Efficient Ontario. Description of Various Programs For Greater Energy Conservation. S.l: s.n, 1986.

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Energy, Ontario Ministry of. Energy trends in Ontario: A five part series. Toronto, ON: Ministry of Energy, 1986.

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Jenkins, Jeremy. A summary report on the design and construction of 5 retrofitted houses and a low energy new house in Armstrong, Ontario: Remote low energy housing demonstration for the Ontario Ministry of Energy. Toronto, Ont: Ontario Ministry of Energy, 1985.

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McRuer, John D. Sustainable energy?: A case study of Ontario :a report submitted to the Conservation Council of Ontario, February 1994. Toronto: Conservation Council of Ontario, 1995.

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University of Texas at Austin. Center for Transportation Research. Texas transportation energy savings: Strategies for reducing energy consumption. Austin, Tex: Texas Sustainabale Energy Development Council, 1995.

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Ivanov, Valentin Borisovich. Puti vovlechenii︠a︡ nevostrebovannykh toplivno-mineralʹnykh resursov Rossii: Sbornik prognozno-analiticheskikh ot︠s︡enok i tekhnologicheskikh predpolozheniĭ za period do 2050 g. Moskva: Novosti teplosnabzhenii︠a︡, 2011.

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Hydro, Ontario. Ontario Hydro's demand/supply plan. Toronto, Ont: Province of Ontario, 1990.

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Book chapters on the topic "Energy conservation – Ontario – Forecasting"

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Copeland, Holly E., Kevin E. Doherty, David E. Naugle, Amy Pocewicz, and Joseph M. Kiesecker. "Forecasting Energy Development Scenarios to Aid in Conservation Design." In Energy Development and Wildlife Conservation in Western North America, 183–93. Washington, DC: Island Press/Center for Resource Economics, 2011. http://dx.doi.org/10.5822/978-1-61091-022-4_10.

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Kumar, Sachin, Saibal K. Pal, and Ram Pal Singh. "Intelligent Energy Conservation: Indoor Temperature Forecasting with Extreme Learning Machine." In Advances in Intelligent Systems and Computing, 977–88. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47952-1_78.

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McDonald, J. R., K. L. Lo, and T. M. Taha. "INCORPORATION OF CONSERVATION MEASURES IN LONG TERM ELECTRICITY DEMAND FORECASTING." In Energy and the Environment, 465–71. Elsevier, 1990. http://dx.doi.org/10.1016/b978-0-08-037539-7.50079-x.

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Conference papers on the topic "Energy conservation – Ontario – Forecasting"

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Norrie, Steven J., and Peter Love. "Creating a culture of conservation in ontario: Approaches, challenges and opportunities." In Energy Society General Meeting (PES). IEEE, 2009. http://dx.doi.org/10.1109/pes.2009.5275904.

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Zareipour, H., K. Bhattacharya, and C. A. Canizares. "Forecasting the hourly Ontario energy price by multivariate adaptive regression splines." In 2006 IEEE Power Engineering Society General Meeting. IEEE, 2006. http://dx.doi.org/10.1109/pes.2006.1709474.

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Anders, George J., and Claudia Rodriguez. "Energy price forecasting and bidding strategy in the Ontario power system market." In 2005 IEEE Russia Power Tech. IEEE, 2005. http://dx.doi.org/10.1109/ptc.2005.4524367.

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Mandal, P., A. U. Haque, Julian Meng, R. Martinez, and A. K. Srivastava. "A hybrid intelligent algorithm for short-term energy price forecasting in the Ontario market." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345461.

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Pany, Prasanta Kumar, and S. P. Ghoshal. "Forecasting the hourly Ontario energy price by local linear wavelet neural network and ARMA models." In the International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2007052.2007091.

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Tao Hong and Pu Wang. "On the impact of demand response: Load shedding, energy conservation, and further implications to load forecasting." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345192.

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Ahmadi, Lena, Woramon Unbangluang, Eric Croiset, Ali Elkamel, Peter L. Douglas, Evgueniy Entchev, and Hong-Ming Ku. "Forecasting the Impact of Plug-In Hybrid Electric Vehicles Penetration on Ontario’s Electricity Grid." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-38240.

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Vehicle emissions are a major concern in the development of new automobiles. Plug-in hybrid electric vehicles (PHEVs) have a large potential to reduce greenhouse gases emissions and increase fuel economy and fuel flexibility. PHEVs are propelled by the energy from both gasoline and electric power sources. Penetration of PHEVs into the automobile market affects the electrical grid and increasing the electricity demand has not been fully investigated. This paper studies effects of the wide spread adoption of PHEVs on peak and base load demands in Ontario, Canada. Long-term forecasting models of peak and base load demands and the number of light-duty vehicles sold are developed. To create proper forecasting models, both linear regression (LR) and non-linear regression (NLR) techniques are employed, considering different ranges in the demographic, climate and economic variables. The results from the LR and NLR models (LRM and NLRM) are compared and the most accurate one is selected. Furthermore, forecasting the effects of PHEVs penetration is done through consideration of various scenarios of penetration levels, such as mild, normal and aggressive ones. Finally, the additional electricity demand on the Ontario electricity grid from charging PHEVs is incorporated for electricity production planning purposes.
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Doiphode, Ganesh, and Hamidreza Najafi. "A Machine Learning Based Approach for Energy Consumption Forecasting in K-12 Schools." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24128.

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Abstract Energy costs are the second highest operational expense for K-12 schools in the United States. Improving energy efficiency and moving towards sustainable school buildings not only result in substantial cost savings and reduction of environmental emissions, but also provides an opportunity to enhance students’ awareness regarding energy, environment, and sustainability. Effective tools and techniques that provide thorough understanding of energy consumption in school buildings are valuable to school districts by helping them with prioritizing energy efficiency projects. In the present paper, a multi-layer perceptron (MLP) neural network model is developed for estimating monthly energy consumption of K-12 schools in Brevard County, Florida. The inputs to the network are considered as number of occupants, days of operation per months, building’s area, average monthly outdoor dry-bulb temperature and relative humidity, as well as the month’s number and the output from the network is monthly energy consumption. Various network topologies are considered and tested to achieve the optimal configuration for the network. The selected network is successfully trained using three years of energy consumption data for 25 schools in Brevard County, FL (high schools, middle schools, and elementary schools). The results showed that the developed neural network model is capable of accurate estimation of monthly energy consumption of schools. The network tested and validated using the data from schools which were not included in the training dataset and the errors between the known values and estimated values for monthly energy consumptions are evaluated and discussed. Although the current study covers one particular school district (Brevard county) in a given climate zone (2a-hot and humid), the developed approach can be extended to incorporate various climate zones and serve as an effective tool for school energy conservation managers. The end user may obtain a clear idea of the energy consumption of the school building and how it compares against other buildings within the same category and climate zone, with minimum input data required.
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Gori, Fabio. "Preliminary Results for Forecasting the Oil Price Evolution With Negative Inflation Rate." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-86729.

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Mass conservation equation is employed to study the time evolution of the mass of oil remaining in a reservoir, according to the mass flow rate of extraction, and to define the critical mass flow rate of extraction, which is the value exhausting the reservoir in an infinite time. The price evolution with time of the resource sold to the market is investigated in case of no-accumulation and no-depletion of the resources; i.e. when the resources are extracted and sold to the market at the same mass flow rate. The energy conservation equation is transformed into an energy-capital conservation equation, which allows to study the oil price evolution with time, dependent on the following parameters. The parameter PIFE, “Price Increase Factor of Extracted resource”, is the difference between the basic interest rate of the capital, e.g. inflation rate, and the mass flow rate of extraction. The parameter PIFS, “Price Increase Factor of Sold resource”, is the difference between the interest rate of the capital, e.g. prime rate, and the mass flow rate of extraction. The parameter CIPS, “Critical Initial Price of Sold resource”, depends on the initial price of the extracted resource, the interest rate of non-extracted resource, and the difference between PIFS and PIFE. The parameter CIPES, “Critical Initial Price Extreme of Sold resource”, depends on the initial price of the extracted resource, the interest rate of non-extracted resource, and PIFS. The present theory is applied to the time evolution of the oil price during the years following the economic crisis of 2008, introducing the new category of cases with a negative inflation rate, that was registered during 2009. The present theory can be applied also to the months with negative inflation rate with a reasonable fair agreement.
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Conry, Patrick, H. J. S. Fernando, L. S. Leo, Ashish Sharma, Mark Potosnak, and Jessica Hellmann. "Multi-Scale Simulations of Climate-Change Influence on Chicago Heat Island." In ASME 2014 4th Joint US-European Fluids Engineering Division Summer Meeting collocated with the ASME 2014 12th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fedsm2014-21581.

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Over the past half century, burgeoning urban areas such as Chicago have experienced elevated anthropogenic-induced alteration of local climates within urbanized regions. As a result, urban heat island (UHI) effect in these areas has intensified. Global climate change can further modulate UHI’s negative effects on human welfare and energy conservation. Various numerical models exist to understand, monitor, and predict UHI and its ramifications, but none can resolve all the relevant physical phenomena that span a wide range of scales. To this end, we have applied a comprehensive multi-scale approach to study UHI of Chicago. The coupling of global, mesoscale, and micro-scale models has allowed for dynamical downscaling from global to regional to city and finally to neighborhood scales. The output of the Community Climate System Model (CCSM5), a general circulation model (GCM), provides future climate scenario, and its coupling with Weather Research and Forecasting (WRF) model enables studies on mesoscale behavior at urban scales. The output from the WRF model at 0.333 km resolution is used to drive a micro-scale model, ENVI-met. Through this coupling the bane of obtaining reliable initial and boundary conditions for the micro-scale model from limited available observational records has been aptly remedied. It was found that the performance of ENVI-met improves when WRF output, rather than observational data, is supplied for initial conditions. The success of the downscaling procedure allowed reasonable application of micro-scale model to future climate scenario provided by CCSM5 and WRF models. The fine (2 m) resolution of ENVI-met enables the study of two key effects of UHI at micro-scale: decreased pedestrian comfort and increased building-scale energy consumption. ENVI-met model’s explicit treatment of key processes that underpin urban microclimate makes it captivating for pedestrian comfort analysis. Building energy, however, is not modeled by ENVI-met so we have developed a simplified building energy model to estimate future cooling needs.
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