Relevant bibliographies by topics / Energy consumption – Ontario – Forecasting

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

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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

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Shirzadi, Navid, Ameer Nizami, Mohammadali Khazen, and Mazdak Nik-Bakht. "Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning." Designs 5, no. 2 (April 6, 2021): 27. http://dx.doi.org/10.3390/designs5020027.

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Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies.
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Sultana, Nahid, S. M. Zakir Hossain, Salma Hamad Almuhaini, and Dilek Düştegör. "Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand." Energies 15, no. 9 (May 7, 2022): 3425. http://dx.doi.org/10.3390/en15093425.

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This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.
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Chen, Jack, Kerry Anderson, Radenko Pavlovic, Michael D. Moran, Peter Englefield, Dan K. Thompson, Rodrigo Munoz-Alpizar, and Hugo Landry. "The FireWork v2.0 air quality forecast system with biomass burning emissions from the Canadian Forest Fire Emissions Prediction System v2.03." Geoscientific Model Development 12, no. 7 (July 26, 2019): 3283–310. http://dx.doi.org/10.5194/gmd-12-3283-2019.

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Abstract. Biomass burning activities can produce large quantities of smoke and result in adverse air quality conditions in regional environments. In Canada, the Environment and Climate Change Canada (ECCC) operational FireWork (v1.0) air quality forecast system incorporates near-real-time biomass burning emissions to forecast smoke plumes from fire events. The system is based on the ECCC operational Regional Air Quality Deterministic Prediction System (RAQDPS) augmented with near-real-time wildfire emissions using inputs from the Canadian Forest Service (CFS) Canadian Wildland Fire Information System (CWFIS). Recent improvements to the representation of fire behaviour and fire emissions have been incorporated into the CFS Canadian Forest Fire Emissions Prediction System (CFFEPS) v2.03. This is a bottom-up system linked to CWFIS in which hourly changes in biomass fuel consumption are parameterized with hourly forecasted meteorology at fire locations. CFFEPS has now also been connected to FireWork. In addition, a plume-rise parameterization based on fire-energy thermodynamics is used to define the smoke injection height and the distribution of emissions within a model vertical column. The new system, FireWork v2.0 (FireWork–CFFEPS), has been evaluated over North America for July–September 2017 and June–August 2018, which are both periods when western Canada experienced historical levels of fire activity with poor air quality conditions in several cities as well as other fires affecting northern Canada and Ontario. Forecast results were evaluated against hourly surface measurements for the three pollutant species used to calculate the Canadian Air Quality Health Index (AQHI), namely PM2.5, O3, and NO2, and benchmarked against the operational FireWork v1.0 system (FireWork-Ops). This comparison shows improved forecast performance and predictive skills for the FireWork–CFFEPS system. Modelled fire-plume injection heights from CFFEPS based on fire-energy thermodynamics show higher plume injection heights and larger variability. The changes in predicted fire emissions and injection height reduced the consistent over-predictions of PM2.5 and O3 seen in FireWork-Ops. On the other hand, there were minimal fire emission contributions to surface NO2, and results from FireWork–CFFEPS do not degrade NO2 forecast skill compared to the RAQDPS. Model performance statistics are slightly better for Canada than for the US, with lower errors and biases. The new system is still unable to capture the hourly variability of the observed values for PM2.5, but it captured the observed hourly variability for O3 concentration adequately. FireWork–CFFEPS also improves upon FireWork-Ops categorical scores for forecasting the occurrence of elevated air pollutant concentrations in terms of false alarm ratio (FAR) and critical success index (CSI).
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Karatzoglidi, Mary, Paraskevas Kerasiotis, and Verena Kantere. "Automated energy consumption forecasting with EnForce." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2771–74. http://dx.doi.org/10.14778/3476311.3476341.

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The need to reduce energy consumption on a global scale has been of high importance during the last years. Research has created methods to make highly accurate forecasts on the energy consumption of buildings and there have been efforts towards the provision of automated forecasting for time series prediction problems. EnForce is a novel system that provides fully automatic forecasting on time series data, referring to the energy consumption of buildings. It uses statistical techniques and deep learning methods to make predictions on univariate or multivariate time series data, so that exogenous factors, such as outside temperature, are taken into account. Moreover, the proposed system provides automatic data preprocessing and, therefore, handles noisy data, with missing values and outliers. EnForce includes full API support and can be used both by experts and non-experts. The proposed demonstration showcases the advantages and technical features of EnForce.
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Pegalajar, M. C., and L. G. B. Ruiz. "Time Series Forecasting for Energy Consumption." Energies 15, no. 3 (January 21, 2022): 773. http://dx.doi.org/10.3390/en15030773.

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Introduction In the last few years, there has been considerable progress in time series forecasting algorithms, which are becoming more and more accurate, and their applications are numerous and varied [...]
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Degefa, Mehari Weldemariam. "Ethiopian energy consumption forecast." Mehran University Research Journal of Engineering and Technology 41, no. 4 (October 1, 2022): 42. http://dx.doi.org/10.22581/muet1982.2204.04.

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This Energy consumption forecast is vital and has a great economic impact. Mathematical models developed for energy forecast can also serve as inputs for further studies. This study is intended to develop an energy consumption forecast using the grey prediction model GM (1,1), based on the actual energy consumption data from the year 2008 to 2017. The models are developed for the total, solid biomass, oil products, and electrical energy consumption; and the accuracy for each model is ratified. These developed forecasting models were used to anticipate six-year Ethiopian consumption of major energy types. The outcomes of models for all four energy consumption types show an upward trend; simulating and forecasting are found suited with the grey system model with development coefficient values less than 0.3 for all selected energy forms.
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Han, Sun, Zhang Xianfeng, and Guo Haixiang. "China’s Energy Consumption Demand Forecasting and Analysis." Journal of Applied Sciences 13, no. 21 (October 15, 2013): 4912–15. http://dx.doi.org/10.3923/jas.2013.4912.4915.

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Ma, Jie, Amos Oppong, Kingsley Nketia Acheampong, and Lucille Aba Abruquah. "Forecasting Renewable Energy Consumption under Zero Assumptions." Sustainability 10, no. 3 (February 25, 2018): 576. http://dx.doi.org/10.3390/su10030576.

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Tomčala, Jiří. "Towards Optimal Supercomputer Energy Consumption Forecasting Method." Mathematics 9, no. 21 (October 23, 2021): 2695. http://dx.doi.org/10.3390/math9212695.

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Accurate prediction methods are generally very computationally intensive, so they take a long time. Quick prediction methods, on the other hand, are not very accurate. Is it possible to design a prediction method that is both accurate and fast? In this paper, a new prediction method is proposed, based on the so-called random time-delay patterns, named the RTDP method. Using these random time-delay patterns, this method looks for the most important parts of the time series’ previous evolution, and uses them to predict its future development. When comparing the supercomputer infrastructure power consumption prediction with other commonly used prediction methods, this newly proposed RTDP method proved to be the most accurate and the second fastest.
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Pavlicko, Michal, Mária Vojteková, and Oľga Blažeková. "Forecasting of Electrical Energy Consumption in Slovakia." Mathematics 10, no. 4 (February 12, 2022): 577. http://dx.doi.org/10.3390/math10040577.

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Prediction of electricity energy consumption plays a crucial role in the electric power industry. Accurate forecasting is essential for electricity supply policies. A characteristic feature of electrical energy is the need to ensure a constant balance between consumption and electricity production, whereas electricity cannot be stored in significant quantities, nor is it easy to transport. Electricity consumption generally has a stochastic behavior that makes it hard to predict. The main goal of this study is to propose the forecasting models to predict the maximum hourly electricity consumption per day that is more accurate than the official load prediction of the Slovak Distribution Company. Different models are proposed and compared. The first model group is based on the transverse set of Grey models and Nonlinear Grey Bernoulli models and the second approach is based on a multi-layer feed-forward back-propagation network. Moreover, a new potential hybrid model combining these different approaches is used to forecast the maximum hourly electricity consumption per day. Various performance metrics are adopted to evaluate the performance and effectiveness of models. All the proposed models achieved more accurate predictions than the official load prediction, while the hybrid model offered the best results according to performance metrics and supported the legitimacy of this research.
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Dissertations / Theses on the topic "Energy consumption – Ontario – Forecasting"

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Bae, Kyungcho. "Energy consumption forecasting: Econometric model vs state space model." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/187010.

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This study examines the forecasting performance of two major multivariate methodologies: econometric modeling and multivariate state space modeling. The same variables are used in both models to facilitate comparison. They are evaluated by both expost and exante accuracy of U.S. energy consumption forecasts. Econometric models are highly simplified and a model selection procedure is applied to the models. Two different formats of multivariate state space models are examined: economic structure and identity structure. Goodrich's algorithm is employed to estimate the state space models. The state space models in both the econometric structure and the identity structure provided generally good estimates, usually, but not always, these forecasts were more accurate than those by the single econometric models.
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He, Miaofen 1976. "Assessing the economic feasibility of a carbon tax on energy inputs in Ontario's pulp and paper industry : an econometric analysis." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31232.

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Knowledge of price responsiveness of energy is important for designing effective price-based controls to curb the GHG emissions in Canada. The translog and logit models are developed in this study to analyze the demand for four types of energy inputs: coal, electricity, natural gas and refined petroleum products in Ontario's pulp and paper industry. The results suggest that the industry is inelastic to price change of energy consumed. Tests indicate that the translog model behaves slightly better than the logit model. The translog model was then applied to study the feasibility of imposing a carbon tax on energy inputs on Ontario's pulp and paper industry, which indicated that this sector does not seem to response to changes in energy inputs prices. Therefore, a carbon tax does not seem to be a good policy option for decreasing greenhouse gas emissions in this sector.
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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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Cheng, Yuanzhi. "Forecasting by learning methods: The gross domestic product, total energy consumption and petroleum consumption of the United States." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186631.

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This study generalizes the applications of learning curve theory. It extends the simple power learning model in two ways: (1) by extending the model to include other sift variables, the extensive learning model; (2) by generalizing the functional relationship to give greater flexibility in modelling the learning curve, the translog learning model. Through empirical analyses of gross domestic product, total energy consumption, petroleum consumption, and petroleum products consumption, different learning curve models are explored and compared.
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Muendej, Krisanee. "Predictions of monthly energy consumption and annual patterns of energy usage for convenience stores by using multiple and nonlinear regression models." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1221.

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Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models are applied to historical data for two years: 2001 and 2002. The approaches are (1) to analyze nonlinear regression models for long term forecasting of annual patterns compared with outdoor temperature, and (2) to analyze multiple regression models for the building type regardless of outdoor temperature. In the first approach, twenty four buildings are categorized as base load group and no base group. Average temperature, cooling efficiencies, and cooling knot temperature are estimated by nonlinear regression models: segment and parabola models. The adjusted r-square results in good performance up to ninety percent accuracy. In the second approach, the other selected six buildings are categorized as no trend group. This group does not respond to outdoor temperature. As the result, multiple a regression model is formed by combination of variables from the nonlinear models and physical building variables of cooling efficiency, cooling temperature, light bulbs, area, outdoor temperature, and orientation of fronts. This model explains up to sixty percent of all convenience stores' data. In conclusion, the accuracy of prediction models is measured by the adjusted r-square results. Among these three models, the multiple regression model shows the highest adjusted r-square (0.597) over the parabola (0.5419) and segment models (0.4806). When the three models come to the application, the multiple regression model is best fit for no trend data type. However, when it is used to predict the energy consumption with the buildings that relate to outdoor temperature, segment and parabola model provide a better prediction result.
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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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Mangisa, Siphumlile. "Statistical analysis of electricity demand profiles." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1011548.

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An electricity demand profile is a graph showing the amount of electricity used by customers over a unit of time. It shows the variation in electricity demand versus time. In the demand profiles, the shape of the graph is of utmost importance. The variations in demand profiles are caused by many factors, such as economic and en- vironmental factors. These variations may also be due to changes in the electricity use behaviours of electricity users. This study seeks to model daily profiles of energy demand in South Africa with a model which is a composition of two de Moivre type models. The model has seven parameters, each with a natural interpretation (one parameter representing minimum demand in a day, two parameters representing the time of morning and afternoon peaks, two parameters representing the shape of each peak, and two parameters representing the total energy per peak). With the help of this model, we trace change in the demand profile over a number of years. The proposed model will be helpful for short to long term electricity demand forecasting.
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Sakva, Denys. "Evaluation of errors in national energy forecasts /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1166.

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Trujillo, Iliana Cardenes. "Quantifying the energy consumption of the water use cycle." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:df481801-cce1-4824-986c-612f4673b8eb.

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The management and delivery of water and wastewater consume significant amounts of energy, mostly in the form of electricity. With increasing populations, climate change, water quality issues and increasing energy prices, it is more important than ever to understand energy consumption patterns. Energy usually represents the largest operational cost in water utilities around the world, yet there is limited work aiming to quantify the specific relationship between water and its associated energy, and understand its implications for future decision-making. This thesis presents variousmethodological approachesto quantify and understand energy use in water infrastructure systems, as well as how to incorporate them in decision-making processes. The main hypotheses are as follows: firstly, a detailed understanding of the use of energy in water infrastructure systems can facilitate more efficient and sustainable water infrastructure systems and, secondly, that incorporating energy into planning for water and wastewater resources can help understand the impacts of decisions and establish trade-offs between actions. To test these hypotheses, the thesis presents an analytical approach to various areas. Firstly, it identifies, maps and quantifies the energy consumption patterns within a water infrastructure system. This is then used to identify inefficiencies and areas of potential energy saving. Secondly, it incorporates detailed energy costs into short and long-term water resources management and planning. Thirdly, it evaluates trade-offs between energy costs and changing effluent quality regulations in wastewater resources. The Thames River basin, in the south-east of England, is used as a case study to illustrate the approach. The results demonstrate that a systematic approach to the quantification of energy use in a water infrastructure system can identify areas of inefficiencies that can be used to make decisions with regards to infrastructure planning. For example, water systems have significant geo-spatial variations in energy consumption patterns that can be addressed specifically to reduce negative trade-offs. The results also show that incorporating detailed energy information into long-term water resources planning can alter the choices made in water supply options, by providing more complete information. Furthermore, methodologically, they show how several methodological approaches can be used to support more complete decision-making in water utilities to reduce short and long-term costs. In this particular case study, the results show that there are important differences in energy consumption by region, and significant differences in the seasonal and energy patterns of water infrastructure systems. For example, water treatment was shown to be the largest consumer of energy within the whole system, compared with pumping or wastewater treatment; but wastewater treatment energy consumption was shown to be the fastest growing over time due to changes in water quality regulatory frameworks. The results show that more stringent effluent standards could result in at least a doubling of electricity consumption and an increase of between 1.29 and 2.30 additional million tonnes of CO2 a year from treating wastewater in large works in the UK. These are projected to continue to increase if the decarbonisation of the electricity grid does not occur fast enough. Finally, the thesis also shows that daily energy consumption can be reduced by up to 18% by optimally routing water through a water network. optimization of water networks, and that a change in discount rates could change the daily operating costs by 19%, that in turn leads to a resulting different set of optimal investment options in a water supply network.
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Books on the topic "Energy consumption – Ontario – Forecasting"

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

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Megatrends for energy efficiency and renewable energy. Lilburn, GA: Fairmont Press, 2011.

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Frisch, Jean-Romain. Future stresses for energy resources: Energy abundance, myth or reality? London: Graham & Trotman, 1986.

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Frisch, Jean-Romain. Future stresses for energy resources: Energy abundance: myth or reality? London: Graham & Trotman, 1986.

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Berg, Sanford V. Forecasts of energy consumption in Florida, 1987-2006. [Gainesville]: Public Utility Research Center, University of Florida, 1989.

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Energy of the future. Moscow: Elma publishers, 2006.

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

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Stamets, Leigh. California transportation energy demand, 1984-2004. [S.l.]: California Energy Commission, Technology Assessments Projects Office, Assessments Division, 1985.

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1934-, Barnes Philip, ed. Energy demand in the developing countries: Prospects for the future. Washington, D.C: World Bank, 1990.

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

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Arghira, Nicoleta, Stéphane Ploix, Ioana Făgărăşan, and Sergiu Stelian Iliescu. "Forecasting Energy Consumption in Dwellings." In Advances in Intelligent Systems and Computing, 251–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32548-9_18.

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Silva, Jose, Isabel Praça, Tiago Pinto, and Zita Vale. "Energy Consumption Forecasting Using Ensemble Learning Algorithms." In Advances in Intelligent Systems and Computing, 5–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23946-6_1.

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Bouabaz, Mohamed, Mourad Mordjaoui, Nabil Bouleknafet, and Badreddine Belghoul. "Forecasting the Energy Consumption Using Neural Network Approach." In Progress in Clean Energy, Volume 1, 311–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16709-1_22.

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Grigoras, Gheorghe. "Electrical Energy Consumption Forecasting to Improve Energy Efficiency of Water Distribution Systems." In Energy Harvesting and Energy Efficiency, 599–628. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49875-1_20.

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Shchetinin, Eugene Yu. "Cluster-Based Energy Consumption Forecasting in Smart Grids." In Developments in Language Theory, 445–56. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99447-5_38.

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Fouad, Mountassir, Reda Mali, and Mohamed Pr.Bousmah. "Machine Learning for Forecasting Building System Energy Consumption." In Learning and Analytics in Intelligent Systems, 235–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36778-7_25.

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Bendali, Wadie, Mohammed Boussetta, Ikram Saber, and Youssef Mourad. "Households Energy Consumption Forecasting with Echo State Network." In Digital Technologies and Applications, 1305–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73882-2_119.

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Ardakani, Samad Ranjbar, Seyed Mohsen Hossein, and Alireza Aslani. "Forecasting Domestic Energy Consumption for the Nordic Countries." In International Solutions to Sustainable Energy, Policies and Applications, 167–209. Lilburn, GA : The Fairmont Press, Inc., [2018]: River Publishers, 2020. http://dx.doi.org/10.1201/9781003150978-10.

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Kouziokas, Georgios N., Alexander Chatzigeorgiou, and Konstantinos Perakis. "Final Energy Consumption Forecasting by Applying Artificial Intelligence Models." In Operational Research in the Digital Era – ICT Challenges, 1–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95666-4_1.

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Weeraddana, Dilusha, Nguyen Lu Dang Khoa, Lachlan O’Neil, Weihong Wang, and Chen Cai. "Energy Consumption Forecasting Using a Stacked Nonparametric Bayesian Approach." In Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 19–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67670-4_2.

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

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Liang, Da-peng, and Dong-hai Ma. "The forecasting of China’s energy consumption." In 2008 International Conference on Management Science and Engineering (ICMSE). IEEE, 2008. http://dx.doi.org/10.1109/icmse.2008.4669112.

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Ragupathi, C., and R. Prakash. "Forecasting energy consumption using enhanced LSTM." In INTERNATIONAL CONFERENCE ON TRENDS IN CHEMICAL ENGINEERING 2021 (ICoTRiCE2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0118176.

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Danilov, Konstantin, and Svetlana Maltseva. "Energy consumption forecasting as a service." In 2021 IEEE 23rd Conference on Business Informatics (CBI). IEEE, 2021. http://dx.doi.org/10.1109/cbi52690.2021.10068.

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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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Chan, Jun Wei, and Chai Kiat Yeo. "Electrical Power Consumption Forecasting with Transformers." In 2022 IEEE Electrical Power and Energy Conference (EPEC). IEEE, 2022. http://dx.doi.org/10.1109/epec56903.2022.10000228.

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Buckler, Jay, Suprio Ray, and Eduardo Castillo-Guerra. "Scalable Local Short-Term Energy Consumption Forecasting." In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, 2019. http://dx.doi.org/10.1109/ccece.2019.8861769.

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Brito, Tiago C., and Miguel A. Brito. "Forecasting of Energy Consumption : Artificial Intelligence Methods." In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2022. http://dx.doi.org/10.23919/cisti54924.2022.9820078.

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Talwariya, Akash, Pushpendra Singh, M. Deva Brinda, Jalpa Jobanputra, and Mohan Lal Kolhe. "Domestic Energy Consumption Forecasting using Machine Learning." In 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE, 2022. http://dx.doi.org/10.23919/splitech55088.2022.9854296.

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Popov, Pavel, and Polina Bochkareva. "Artificial neural network energy consumption forecasting system." In 13TH INTERNATIONAL SCIENTIFIC CONFERENCE ON AERONAUTICS, AUTOMOTIVE AND RAILWAY ENGINEERING AND TECHNOLOGIES (BulTrans-2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0099798.

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Zhang, Xiaoou Monica, Katarina Grolinger, Miriam A. M. Capretz, and Luke Seewald. "Forecasting Residential Energy Consumption: Single Household Perspective." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00024.

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Reports on the topic "Energy consumption – Ontario – Forecasting"

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Mikayilov, Jeyhun, Ryan Alyamani, Abdulelah Darandary, Muhammad Javid, Fakhri Hasanov, Saleh T. AlTurki, and Rey B. Arnaiz. Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics. King Abdullah Petroleum Studies and Research Center, January 2022. http://dx.doi.org/10.30573/ks--2021-dp19.

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Abstract:
The objective of this study is to investigate Saudi Arabia’s industrial electricity consumption at the regional level. We apply structural time series modeling to annual data over the period of 1990 to 2019. In addition to estimating the size and significance of the price and income elasticities for regional industrial electricity demand, this study projects regional industrial electricity demand up to 2030. This is done using estimated equations and assuming different future values for price and income. The results show that the long-run income and price elasticities of industrial electricity demand vary across regions. The underlying energy demand trend analysis indicates some efficiency improvements in industrial electricity consumption patterns in all regions.
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Mikayilov, Jeyhun, Ryan Alyamani, Abdulelah Darandary, Muhammad Javid, and Fakhri Hasanov. Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics. King Abdullah Petroleum Studies and Research Center, January 2022. http://dx.doi.org/10.30573/ks--2021-dp22.

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Abstract:
The objective of this study is to investigate Saudi Arabia’s industrial electricity consumption at the regional level. We apply structural time series modeling to annual data over the period of 1990 to 2019. In addition to estimating the size and significance of the price and income elasticities for regional industrial electricity demand, this study projects regional industrial electricity demand up to 2030. This is done using estimated equations and assuming different future values for price and income. The results show that the long-run income and price elasticities of industrial electricity demand vary across regions. The underlying energy demand trend analysis indicates some efficiency improvements in industrial electricity consumption patterns in all regions.
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