Journal articles on the topic 'Energy conservation – Ontario – Forecasting'
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1
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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Sarmas, Elissaios, Sofoklis Strompolas, Vangelis Marinakis, Francesca Santori, Marco Antonio Bucarelli, and Haris Doukas. "An Incremental Learning Framework for Photovoltaic Production and Load Forecasting in Energy Microgrids." Electronics 11, no. 23 (November 29, 2022): 3962. http://dx.doi.org/10.3390/electronics11233962.
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Energy management is crucial for various activities in the energy sector, such as effective exploitation of energy resources, reliability in supply, energy conservation, and integrated energy systems. In this context, several machine learning and deep learning models have been developed during the last decades focusing on energy demand and renewable energy source (RES) production forecasting. However, most forecasting models are trained using batch learning, ingesting all data to build a model in a static fashion. The main drawback of models trained offline is that they tend to mis-calibrate after launch. In this study, we propose a novel, integrated online (or incremental) learning framework that recognizes the dynamic nature of learning environments in energy-related time-series forecasting problems. The proposed paradigm is applied to the problem of energy forecasting, resulting in the construction of models that dynamically adapt to new patterns of streaming data. The evaluation process is realized using a real use case consisting of an energy demand and a RES production forecasting problem. Experimental results indicate that online learning models outperform offline learning models by 8.6% in the case of energy demand and by 11.9% in the case of RES forecasting in terms of mean absolute error (MAE), highlighting the benefits of incremental learning.
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Jaffé, Rodolfo, Samia Nunes, Jorge Filipe Dos Santos, Markus Gastauer, Tereza C. Giannini, Wilson Nascimento Jr, Marcio Sales, Carlos M. Souza, Pedro W. Souza-Filho, and Robert J. Fletcher. "Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts." Environmental Research Letters 16, no. 8 (July 29, 2021): 084034. http://dx.doi.org/10.1088/1748-9326/ac146a.
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Moran, Lesley A., and Jagdish C. Nautiyal. "Present and future feasibility of short-rotation energy farms in Ontario." Forest Ecology and Management 10, no. 4 (May 1985): 323–38. http://dx.doi.org/10.1016/0378-1127(85)90123-9.
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Wang, Zhao Han, Chang Guo Xuan, and Qiang Fu. "Urban Water Consumption Forecasting Based on Projection Pursuit." Advanced Materials Research 542-543 (June 2012): 1334–38. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1334.
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In order to meet energy conservation and utilization optimization in urban water supply system and ensure water supply time and efficiency, we adopted the projection pursuit autoregression method to establish the projection pursuit autoregression water consumption forecasting model, combined the projection pursuit technique and time-series autoregression analysis method, and better solved the abnormal and nonlinear problems in urban water consumption. The practice proves that the forecasting precision of this model reaches the engineering requirements, and it provides a new approach for water consumption forecasting and analysis.
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Conway, Christopher D. "Ontario’s Electrical Future: Global Environmental Limits, Systems Thinking, and Electrical Power Planning in Ontario, 1974-1983." Scientia Canadensis 37, no. 1-2 (May 20, 2015): 34–58. http://dx.doi.org/10.7202/1030639ar.
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In the mid-1970s, the Royal Commission on Electrical Power Planning (RCEPP) was ordered by the government of Ontario to review Ontario Hydro’s ambitious expansion plans. Historians have often considered the RCEPP an interesting but ineffective commission as changing economic factors, rather than the Commissions’ recommendations for slower growth, eventually slowed Hydro’s momentum in the early 1980s. This paper explores the Commission as an important venue for energy debate and as a means of facilitating research from public interest groups, including Energy Probe, in the late 1970s. From this debate the Commission negotiated ideas of “soft energy paths”, global resource limits, and cybernetic system thinking into a set of policy recommendations for democratic, systems-based electrical power planning. I argue that the tension between centralized control and local action found in the Commission’s systems approach to planning illustrates the difficulty of collective, long-term, and expert mediated, globalist planning in a period once thought of as a “dawning age of energy conservation.”
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Duan, Yun. "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning." Sustainability 14, no. 14 (July 13, 2022): 8584. http://dx.doi.org/10.3390/su14148584.
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Energy conservation in buildings has increasingly become a hot issue for the Chinese government. Compared to deterministic load prediction, probabilistic load forecasting is more suitable for long-term planning and management of building energy consumption. In this study, we propose a probabilistic load-forecasting method for daily and weekly indoor load. The methodology is based on the long short-term memory (LSTM) model and penalized quantile regression (PQR). A comprehensive analysis for a time period of a year is conducted using the proposed method, and back propagation neural networks (BPNN), support vector machine (SVM), and random forest are applied as reference models. Point prediction as well as interval prediction are adopted to roundly test the prediction performance of the proposed model. Results show that LSTM-PQR has superior performance over the other three models and has improvements ranging from 6.4% to 20.9% for PICP compared with other models. This work indicates that the proposed method fits well with probabilistic load forecasting, which could promise to guide the management of building sustainability in a future carbon neutral scenario.
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Zhang, Qi, Xiao Ying Wang, Da Wei Zhang, Tao Du, and Jiu Ju Cai. "Development of Energy Management System in Integrated Iron and Steel Works." Advanced Materials Research 204-210 (February 2011): 1737–40. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1737.
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Energy management system (EMS) will be one of energy-saved technologies for iron and steel route. The paper analyzes EMS structure and development focusing on the energy forecasting, optimization and other key technologies in iron and steel works. Taking gas management subsystem of EMS as an example, the forecasting and optimization are described. Byproduct gas is one of important energy medium in energy system, which can play a significant role in energy savings in iron and steel works. In this paper, the models of byproduct gas generation, consumption prediction and optimal utilization are developed for predicting and distributing byproduct gases to make them emit zero. The results show that: EMS should have hardware and software technology conditions to exert its functions; Energy medium, such as byproduct gas and steam, prediction and optimization will be play an important role in energy conservation and emission reduction.
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Li, Wan Zhen. "The Study on Building Energy Conservation Technology Based on Four-Layer Neural Network." Applied Mechanics and Materials 246-247 (December 2012): 428–32. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.428.
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This paper build general regression neural network building conservation technology project appraisal model, Written software programs with MATLAB7.0 neural network toolbox to appraise building conservation technology project, trained and tested network by 17 ventilation samples, shows that GRNN have a good feasibility for appraisal. The main advantage of the GRNN model reflects four aspects. First of all, GRNN is a very simple and fast learning procedure thus it has less training time. Second, it is unnecessary to define the number of hidden layers or the number of neurons per layer in advance. Third, GRNN can handle linear and nonlinear data. Fourth, adding new samples to the training set does not require re-calibrating the model. Finally, it has only one adjustable parameter thereby making overtraining less likely. Because of these advantages, GRNN can be applied to many fields with other forecasting approaches or appraisal. GRNN building conservation technology project model building and training, testing can use neural network toolbox of MATLAB software.
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Pearce, Joshua M. "Agrivoltaics in Ontario Canada: Promise and Policy." Sustainability 14, no. 5 (March 4, 2022): 3037. http://dx.doi.org/10.3390/su14053037.
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Well-intentioned regulations to protect Canada’s most productive farmland restrict large-scale solar photovoltaic (PV) development. The recent innovation of agrivoltaics, which is the co-development of land for both PV and agriculture, makes these regulations obsolete. Burgeoning agrivoltaics research has shown agricultural benefits, including increased yield for a wide range of crops, plant protection from excess solar energy and hail, and improved water conservation, while maintaining agricultural employment and local food supplies. In addition, the renewable electricity generation decreases greenhouse gas emissions while increasing farm revenue. As Canada, and Ontario in particular, is at a strategic disadvantage in agriculture without agrivoltaics, this study investigates the policy changes necessary to capitalize on the benefits of using agrivoltaics in Ontario. Land-use policies in Ontario are reviewed. Then, three case studies (peppers, sweet corn, and winter wheat) are analysed for agrivoltaic potential in Ontario. These results are analysed in conjunction with potential policies that would continue to protect the green-belt of the Golden Horseshoe, while enabling agrivoltaics in Ontario. Four agrivoltaic policy areas are discussed: increased research and development, enhanced education/public awareness, mechanisms to support Canada’s farmers converting to agrivoltaics, and using agrivoltaics as a potential source of trade surplus with the U.S.
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Zakonnova, Ludmila, Igor Nikishkin, Ludmila Stemplewska, and Alena Chupryakova. "Principles of Conservation of Biodiversity in Hydrobiocenoses Formed as a Result of Carbon and Energy Enterprises." E3S Web of Conferences 174 (2020): 02027. http://dx.doi.org/10.1051/e3sconf/202017402027.
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This paper presents the principles developed by the authors for the operation of reservoirs of technogenic origin, formed as a result of the activities of coal mining and energy enterprises, which can become basic when developing models for the rational use of wastewater and land from coal mining regions: maximum environmental friendliness, reasonable technological (biotechnological) restrictions, forecasting and regulation ecological consequences of the introduction of alien objects of ichthyofauna, etc. The expediency of soft biological methods for cleaning eutrophic water bodies is substantiated. As part of the implementation of the principle of forecasting and regulating the environmental consequences of the introduction of alien fauna ichthyofauna, a new approach is proposed to create a model of variability of the hydroecosystem that will allow developing mechanisms to maintain ecological balance in water bodies and coordinate the work of hatcheries and fishing enterprises, environmental monitoring services and other institutions. The principles of a reasonable biotechnological restriction in the operation of a reservoir and the possibility of alternative use of a reservoir have found their application in the development of warm-water aquaculture using waste warm water. It is proved that the principles of operation of reservoirs of technogenic origin, formed as a result of the activities of coal mining and energy enterprises, may well be successfully implemented to preserve biological diversity in large technogenic reservoirs.
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Hwang, Pyeong-Ik, Seong-Chul Kwon, and Sang-Yun Yun. "Schedule-Based Operation Method Using Market Data for an Energy Storage System of a Customer in the Ontario Electricity Market." Energies 11, no. 10 (October 9, 2018): 2683. http://dx.doi.org/10.3390/en11102683.
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A new operation method for an energy storage system (ESS) was proposed to reduce the electricity charges of a customer paying the wholesale price and participating in the industrial conservation initiative (ICI) in the Ontario electricity market of Canada. Electricity charges were overviewed and classified into four components: fixed cost, electricity usage cost, peak demand cost, and Ontario peak contribution cost (OPCC). Additionally, the online market data provided by the independent electricity system operator (IESO), which operates the Ontario electricity market, were reviewed. From the reviews, it was identified that (1) the portion of the OPCC in the electricity charges increased continuously, and (2) large errors can sometimes exist in the forecasted data given by the IESO. In order to reflect these, a new schedule-based operation method for the ESS was proposed in this paper. In the proposed method, the operation schedule for the ESS is determined by solving an optimization problem to minimize the electricity charges, where the OPCC is considered and the online market data provided by the IESO is used. The active power reference for the ESS is then calculated from the scheduled output for the current time interval. To reflect the most recent market data, the operation schedule and the active power reference for the ESS are iteratively determined for every five minutes. In addition, in order to cope with the prediction errors, methods to correct the forecasted data for the current time interval and secure the energy reserve are presented. The results obtained from the case study and actual operation at the Penetanguishene microgrid test bed in Ontario are presented to validate the proposed method.
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Wan Abdul Razak, Intan Azmira, Izham Zainal Abidin, Yap Keem Siah, and Mohamad Fani Sulaima. "NEXT-HOUR ELECTRICITY PRICE FORECASTING USING LEAST SQUARES SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM." ASEAN Engineering Journal 12, no. 3 (August 31, 2022): 11–17. http://dx.doi.org/10.11113/aej.v12.17276.
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Predicting the price of electricity is crucial for the operation of power systems. Short-term electricity price forecasting deals with forecasts from an hour to a day ahead. Hourly-ahead forecasts offer expected prices to market participants before operation hours. This is especially useful for effective bidding strategies where the bidding amount can be reviewed or changed before the operation hours. Nevertheless, many existing models have relatively low prediction accuracy. Furthermore, single prediction models are typically less accurate for different scenarios. Thus, a hybrid model comprising least squares support vector machine (LSSVM) and genetic algorithm (GA) was developed in this work to predict electricity prices with higher accuracy. This model was tested on the Ontario electricity market. The inputs, which were the hourly Ontario electricity price (HOEP) and demand for the previous seven days, as well as 1-h pre-dispatch price (PDP), were optimized by GA to prevent losing potentially important inputs. At the same time, the LSSVM parameters were optimized by GA to obtain accurate forecasts. The hybrid LSSVM-GA model was shown to produce an average mean absolute percentage error (MAPE) of 8.13% and the structure of this model is less complex compared with other models developed in previous studies. This is due to the fact that only two algorithms were used (LSSVM and GA), with the load and HOEP for the week preceding the forecasting hour as the inputs. Based on the results, it is concluded that the proposed hybrid algorithm is a promising alternative to produce good electricity price forecasts.
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Chen, Yuzhen, Suzhen Li, and Shuangbing Guo. "A Novel Fractional Hausdorff Discrete Grey Model for Forecasting the Renewable Energy Consumption." Journal of Mathematics 2022 (October 27, 2022): 1–23. http://dx.doi.org/10.1155/2022/8443619.
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Reducing carbon dioxide emissions and using renewable energy to replace fossil fuels have become an essential trend in future energy development. Renewable energy consumption has a significant impact on energy security so accurate prediction of renewable energy consumption can help the energy department formulate relevant policies and adjust the energy structure. Based on this, a novel Fractional Hausdorff Discrete Grey Model, abbreviated FHDGM (1,1), is developed in this study. The paper investigates the model’s characteristics. The fractional-order r of the FHDGM (1,1) model is optimized using particle swarm optimization. Subsequently, through two empirical analyses, the prediction accuracy of the FHDGM (1,1) model is proven to be higher than that of other models. Finally, the proposed model is applied with a view to forecasting the consumption of renewable energy for the years 2021 to 2023 in three different areas: the Asia Pacific region, Europe, and the world. The study’s findings will offer crucial forecasting data for worldwide energy conservation and emission reduction initiatives.
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Li, Kangji, Wenping Xue, Gang Tan, and Anthony S. Denzer. "A state of the art review on the prediction of building energy consumption using data-driven technique and evolutionary algorithms." Building Services Engineering Research and Technology 41, no. 1 (May 3, 2019): 108–27. http://dx.doi.org/10.1177/0143624419843647.
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Energy consumption forecasting for buildings plays a significant role in building energy management, conservation and fault diagnosis. Owing to the ease of use and adaptability of optimal solution seeking, data-driven techniques have proved to be accurate and efficient tools in recent years. This study provides a comprehensive review on the existing data-driven approaches for building energy forecasting, such as regression models, artificial neural networks, support vector machines, fuzzy models, grey models, etc. On this basis, the paper puts emphasis to the discussion on evolutionary algorithms hybridized models that combine evolutionary algorithms with regular data-driven models to improve prediction accuracy and robustness. Various combinations of such hybrid models are classified and their characteristics are analyzed. Finally, a detailed discussion on the advantages and challenges of current predictive models is provided. Practical Application: Building energy consumption prediction is important for building energy management, efficiency and fault diagnosis. For existing buildings, multisourced, heterogeneous or inadequate data-driven models may lead to convergence problem or poor model accuracy. To this end, a state of art review on building energy forecasting technique is helpful for related professionals in the building industry.
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Kassem, Sameh A., Abdulla H. A. EBRAHIM, Abdulla M. Khasan, and Alla G. Logacheva. "FORECASTING ELECTRIC CONSUMPTION OF THE ENTERPRISE USING ARTIFICIAL NEURAL NETWORKS." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 1 (2021): 177–93. http://dx.doi.org/10.21684/2411-7978-2021-7-1-177-193.
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Energy consumption has increased dramatically over the past century due to many factors, including both technological, social and economic factors. Therefore, predicting energy consumption is of great importance for many parameters, including planning, management, optimization and conservation. Data-driven models for predicting energy consumption have grown significantly over the past several decades due to their improved performance, reliability, and ease of deployment. Artificial neural networks are among the most popular data-driven approaches among the many different types of models today. This article discusses the possibility of using artificial neural networks for medium-term forecasting of the power consumption of an enterprise. The task of constructing an artificial neural network using a feedback algorithm for training a network based on the Matlab mathematical package has been implemented. The authors have analyzed such characteristics as parameter setting, implementation complexity, learning rate, convergence of the result, forecasting accuracy, and stability. The results obtained led to the conclusion that the feedback algorithm is well suited for medium-term forecasting of power consumption.
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Pourhaji, Nazila, Mohammad Asadpour, Ali Ahmadian, and Ali Elkamel. "The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study." Sustainability 14, no. 5 (March 6, 2022): 3063. http://dx.doi.org/10.3390/su14053063.
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The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.
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Zhang, Yin Juan. "Research of Dynamic Forecasting Model of Temperature Field in Slab and ANSYS Analysis." Advanced Materials Research 781-784 (September 2013): 2546–49. http://dx.doi.org/10.4028/www.scientific.net/amr.781-784.2546.
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In view of the practical significance of the establishment of model database of slab heating, the dynamic forecasting model of temperature field in slab is established by the numerical solution according to the energy conservation in this paper. The ANSYS simulation has been successfully introduced to the modeling simulation of slab heating based on the character of slab heating. The simulation results emerge the temperature field and heat transfer of slab heating, and offer investigative instrument for the more analyse of slab heating and heating system. The temperature field in slab and energy saving would be achieved by using the model database of slab heating.
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Turnbull, Thomas. "‘No Solution to the Immediate Crisis’: The Uncertain Political Economy of Energy Conservation in 1970s Britain." Contemporary European History 31, no. 4 (November 2022): 570–92. http://dx.doi.org/10.1017/s0960777322000625.
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This article traces one aspect of Britain's approach to the political economy of energy conservation. It focuses on the forecasting work of Royal Dutch Shell and the deliberations of the Heath government. In the late 1960s, the oil major Shell predicted that oil-producing states would impose an embargo on oil-consuming states. Energy conservation policies would be necessary. In tracing the reception of Shell's ‘crisis’ scenario and its proposed resolution, this article details how these ideas were received by Edward Heath's Conservative government, particularly its ‘think-tank’, the Central Policy Review Staff. In the short term, interventionist policies were proposed so as to demonstrate Britain's ability to operate without ever-increasing oil consumption, while in the long term the idea was that the energy-saving capacities of a freely-operating market could address the problem. The article recounts the confusion these proposed conservation policies provoked, and how the second idea gradually coalesced and ultimately outlasted the Heath government, providing one justification for the eventual privatisation of Britain's formerly nationalised energy industries.
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Liu, Chunxia, and Liqun Liu. "The global peak forecasting method for PV array based on the conservation of energy at uniform and partial shading." Energy Reports 6 (December 2020): 113–22. http://dx.doi.org/10.1016/j.egyr.2020.12.002.
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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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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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El Maghraoui, Adila, Younes Ledmaoui, Oussama Laayati, Hicham El Hadraoui, and Ahmed Chebak. "Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine." Energies 15, no. 13 (June 22, 2022): 4569. http://dx.doi.org/10.3390/en15134569.
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The mining industry’s increased energy consumption has resulted in a slew of climate-related effects on the environment, many of which have direct implications for humanity’s survival. The forecast of mine site energy use is one of the low-cost approaches for energy conservation. Accurate predictions do indeed assist us in better understanding the source of high energy consumption and aid in making early decisions by setting expectations. Machine Learning (ML) methods are known to be the best approach for achieving desired results in prediction tasks in this area. As a result, machine learning has been used in several research involving energy predictions in operational and residential buildings. Only few research, however, has investigated the feasibility of machine learning algorithms for predicting energy use in open-pit mines. To close this gap, this work provides an application of machine learning algorithms in the RapidMiner tool for predicting energy consumption time series using real-time data obtained from a smart grid placed in an experimental open-pit mine. This study compares the performance of four machine learning (ML) algorithms for predicting daily energy consumption: Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The models were trained, tested, and then evaluated. In order to assess the models’ performance four metrics were used in this study, namely correlation (R), mean absolute error (MAE), root mean squared error (RMSE), and root relative squared error (RRSE). The performance of the models reveals RF to be the most effective predictive model for energy forecasting in similar cases.
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Sommer, Matthias, and Sebastian Reich. "Phase Space Volume Conservation under Space and Time Discretization Schemes for the Shallow-Water Equations." Monthly Weather Review 138, no. 11 (November 1, 2010): 4229–36. http://dx.doi.org/10.1175/2010mwr3323.1.
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Abstract Applying concepts of analytical mechanics to numerical discretization techniques for geophysical flows has recently been proposed. So far, mostly the role of the conservation laws for energy- and vorticity-based quantities has been discussed, but recently the conservation of phase space volume has also been addressed. This topic relates directly to questions in statistical fluid mechanics and in ensemble weather and climate forecasting. Here, phase space volume behavior of different spatial and temporal discretization schemes for the shallow-water equations on the sphere are investigated. Combinations of spatially symmetric and common temporal discretizations are compared. Furthermore, the relation between time reversibility and long-time volume averages is addressed.
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Sajid, Zaman, Asma Javaid, Muhammad Kashif Khan, Hamad Sadiq, and Usman Hamid. "Integration of Regression Analysis and Monte Carlo Simulation for Probabilistic Energy Policy Guidelines in Pakistan." Resources 10, no. 9 (August 25, 2021): 88. http://dx.doi.org/10.3390/resources10090088.
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Forecasting energy demand and supply is the most crucial concern for energy policymakers. However, forecasting may introduce uncertainty in the energy model, and an energy policy based on an uncertain model could be misleading. Without certainty in energy data, investors cannot quantify risk and trade-offs, which are compulsory for investments in energy projects. In this work, the energy policies of Pakistan are taken as a case study, and flaws in its energy policymaking are identified. A novel probabilistic model integrated with curve fitting methods was proposed and was applied to 17 different energy demand and supply variables. Monte Carlo simulation (MCS) was performed to develop probabilistic energy profiles for each year from 2017 to 2050. Results show that the forecasted energy supply of Pakistan in the years 2025 and 2050 would be 70.69 MTOE and 131.65 MTOE, respectively. The probabilistic analysis showed that there is 14% and 6% uncertainty in achieving these targets. The research shows the expected energy consumption of 70.33 MTOE and 189.48 MTOE in 2025 and 2050, respectively, indicating uncertainties of 65% and 31%. Based on the results, eight energy policy guidelines and recommendations are provided for sustainable energy resource management. This study recommends developing a robust and sustainable energy policy for Pakistan with the help of transparent governance.
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Zhou, Jianguo, Xiaolei Xu, Wei Li, Fengtao Guang, Xuechao Yu, and BaoLing Jin. "Forecasting CO2 Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis." Journal of Energy 2019 (March 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/8275491.
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As a pillar industry of national economy, China’s construction industry is still facing the status of substantial energy consumption and high CO2 emissions, which is a key field of energy conservation and emission reduction. In CO2 emissions research, it is essential to focus on analyzing the present and future trends of CO2 emissions in China’s construction industry. This article introduces a novel prediction model, in which the weighted algorithm is combined with Elman neural network (ENN) optimized by Adaptive Boosting algorithm (Adaboost) for evaluating future CO2 emissions in China’s construction industry. Firstly, logarithmic mean Divisia index (LMDI) is used to decompose CO2 emissions into economy, structural, intensity, and population indicators, posing as inputs to the weighted Adaboost-ENN model. Then, through comparison with other three models based on the data of total CO2 emissions in China’s construction industry during 2004-2016, there is evidence that the proposed model makes a favorable prediction performance. On this basis, we employ scenario analysis to predict future trend of CO2 emissions in China’s construction industry. It can be found that the peak of CO2 emissions in China’s construction industry will be achieved before 2030 in high carbon scenario (HS) and baseline carbon scenario (BS), whereas it will not be realized in low carbon scenario (LS). Finally, the specific policy recommendations related to energy conservation and emission reduction in China’s construction industry are proposed.
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Dvinin, D., A. Davankov, and A. Plaksina. "Methodological toolkit for assessing the impact of renewable energy on the balanced development and preservation of the regional carbon cycle." IOP Conference Series: Earth and Environmental Science 1070, no. 1 (July 1, 2022): 012010. http://dx.doi.org/10.1088/1755-1315/1070/1/012010.
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Abstract The article provides methodological tools that allow to carry out a socio-ecological and economic assessment of the balance of regions in the development of renewable (alternative) energy sources. When forecasting the development of renewable energy in the region, it was taken into account that, unlike fossil fuel-based energy, it does not have a significant impact on the environment, since it uses material and energy flows circulating in natural systems. During the study, it was found that only one transition to renewable energy sources will not allow achieving carbon balance in the regions. The landscape features of the region significantly affect the possibility of its conservation. Therefore, in each region, it will be necessary to identify the area of territories with a special nature conservation status that will allow maintaining the carbon cycle. Methodological tools were tested on the example of the Chelyabinsk region of the Russian Federation. As a result, it was found to maintain a balanced carbon cycle, it will be necessary to increase the share of natural steppes to 49.1% of the total area of the region, and achieve the value of renewable energy in the regional energy balance up to 93%.
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Ren, Hui, Jia Qi Fan, David Watts, and Dan Wei. "Quantifying the Risk of Wind Power Forecasting Error on Power System Optimal Dispatching." Applied Mechanics and Materials 672-674 (October 2014): 355–60. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.355.
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With large-scale wind power integrates into power system, the risk brought by the uncertainty of wind power output can no longer be neglected. Under this circumstance, the operation risk due to the uncertainty of wind generation and the contribution of wind power to energy conservation and emission reduction are quantified, and the corresponding quantified operational cost, environmental cost and operation risk are being integrated into the economic dispatching model to establish a multi-objective optimization dispatch model. Non-dual interior point method is used to solve the optimization problem. The method is applied to Hebei Southern power grid, simulated with actual wind power output data of one typical day. Simulation results show the rationality and effectiveness of the proposed method.
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Lingyu, Liang, Wenqi Huang, Zhaojie Dong, Jiguang Zhao, Peng Li, Bingfang Lu, and Xinde Zhu. "Short-term power load forecasting based on combined kernel Gaussian process hybrid model." E3S Web of Conferences 256 (2021): 01009. http://dx.doi.org/10.1051/e3sconf/202125601009.
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As one of the countries with the most energy consumption in the world, electricity accounts for a large proportion of the energy supply in our country. According to the national basic policy of energy conservation and emission reduction, it is urgent to realize the intelligent distribution and management of electricity by prediction. Due to the complex nature of electricity load sequences, the traditional model predicts poor results. As a kernel-based machine learning model, Gaussian Process Mixing (GPM) has high predictive accuracy, can multi-modal prediction and output confidence intervals. However, the traditional GPM often uses a single kernel function, and the prediction effect is not optimal. Therefore, this paper will combine a variety of existing kernel to build a new kernel, and use it for load sequence prediction. In the electricity load prediction experiments, the prediction characteristics of the load sequences are first analyzed, and then the prediction is made based on the optimal hybrid kernel function constructed by GPM and compared with the traditional prediction model. The results show that the GPM based on the hybrid kernel is not only superior to the single kernel GPM but also superior to some traditional prediction models such as ridge regression, kernel regression and GP.
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Chen, Linyan, Albert P. C. Chan, Qiang Yang, Amos Darko, and Xin Gao. "Forecasting Green Building Growth in Different Regions of China." IOP Conference Series: Earth and Environmental Science 1101, no. 2 (November 1, 2022): 022042. http://dx.doi.org/10.1088/1755-1315/1101/2/022042.
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Abstract Green building has significant merits in energy conservation and resource efficiency, making it prevalent in many countries. Forecasting green building growth helps governments develop relevant policies and benefits researchers to solve the problem of lack of data. Although there were various studies on green building development, few forecasted growth to inform green building policy. To fill the gap, this study aims to develop an innovative approach to predict green building growth in different regions of China. A long short-term memory (LSTM) model with an attention mechanism was put forward in this study. Results show that the innovative model performed well in forecasting green building growth. The green building development in China keeps an increasing trend and will continue the growth at a higher speed in the following years. Moreover, geographical clustering patterns of green buildings were investigated, and a three-step distribution pattern was observed. Although this research was conducted in the Chinese context, it provides references to other countries by proposing an innovative model, which helps them better understand the patterns of green building growth. This study developed an innovative approach to forecasting green buildings, contributing to the existing green building knowledge body. Furthermore, it benefits governments and practitioners in decision-making.
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Bonter, David N., Therese M. Donovan, and Elizabeth W. Brooks. "Daily Mass Changes in Landbirds During Migration Stopover on the South Shore of Lake Ontario." Auk 124, no. 1 (January 1, 2007): 122–33. http://dx.doi.org/10.1093/auk/124.1.122.
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Abstract Assigning conservation priorities to areas used by birds during migration requires information on the relative quality of areas and habitats. The rate at which migratory birds replenish energy reserves during stopover may be used as an indicator of stopover-site quality. We estimated the rate of mass gain of 34 landbird species during stopover at a near-shore terrestrial site on the south shore of Lake Ontario in New York during 12 migration seasons from 1999 to 2004. The average rate of mass gain was estimated by relating a measure of condition to time of capture (hour after sunrise) with linear regression. Data from 25,385 captures were analyzed. Significantly positive rates of mass change were detected for 20 of 30 species during spring migration and 19 of 21 species during autumn migration. No significantly negative trends were detected in either season. Daily rates of mass gain across all species averaged 9.84% of average lean body weight during spring migration and 9.77% during autumn migration. Our regression estimates were significantly greater than estimates from traditional analyses that examine mass changes in recaptured birds. Analyses of mass changes in recaptured birds revealed a mean daily change of −0.68% of average lean mass in spring and 0.13% in autumn. Because of sampling biases inherent in recapture analyses, the regression approach is likely more accurate when the assumptions of the method are met. Similar studies in various habitats, landscapes, and regions are required to prioritize conservation efforts targeting migratory stages of the annual cycle. Cambios de Peso Diarios de Aves Terrestres durante las Paradas Migratorias en la Costa sur del Lago Ontario
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Jia, Ni Sha, Yong Hui Han, and Bo Hu. "Research on the Development of China’s Emission Reduction Based on Low Carbon Economy." Advanced Materials Research 962-965 (June 2014): 2381–85. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2381.
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This paper built up a forecasting model for CO2 emission and calcutaled the CO2 emissions of 30 provinces (cities) in China. It’s been found that the CO2 emission amount varies greatly in different provinces (cities). We find that the capacity of energy conservation and emmision reduction of eastern region is best, the western region is in the middle level, while the central region is not good enough. Based on the above, this paper proposed concrete suggestions on energy saving and emission reduction.They are not only in line the national policies but also have took consideration of the economic and social development of different areas.
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Chrysikopoulos, Stamatis, and Panos Chountalas. "Integrating energy and environmental management systems to enable facilities to qualify for carbon funds." Energy & Environment 29, no. 6 (March 15, 2018): 938–56. http://dx.doi.org/10.1177/0958305x18762586.
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The purpose of this paper is to propose a practical framework that integrates energy and environmental management systems to satisfy the monitoring and verification requirements of facilities energy conservation and greenhouse gas emissions reduction; these requirements are essential for organisations to access financing mechanisms, such as carbon funds. As a reference point, the framework uses the ISO 50001 standard, which pertains to an organisation’s energy management procedures. This framework is enriched with elements from other standards, such as ISO 14001 (environmental management system) and ISO 14064 (GHG verification system). The framework also incorporates sound technology management practices and other obligations, such as those arising from international law. It, thus, allows for the systematic quantification, assessment and forecasting of the energy and environmental footprints of facilities throughout their life cycles, enabling them to qualify for carbon funds.
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Zhukovskiy, Yuriy, Pavel Tsvetkov, Aleksandra Buldysko, Yana Malkova, Antonina Stoianova, and Anastasia Koshenkova. "Scenario Modeling of Sustainable Development of Energy Supply in the Arctic." Resources 10, no. 12 (December 7, 2021): 124. http://dx.doi.org/10.3390/resources10120124.
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The 21st century is characterized not only by large-scale transformations but also by the speed with which they occur. Transformations—political, economic, social, technological, environmental, and legal-in synergy have always been a catalyst for reactions in society. The field of energy supply, like many others, is extremely susceptible to the external influence of such factors. To a large extent, this applies to remote (especially from the position of energy supply) regions. The authors outline an approach to justifying the development of the Arctic energy infrastructure through an analysis of the demand for the amount of energy consumed and energy sources, taking into account global trends. The methodology is based on scenario modeling of technological demand. It is based on a study of the specific needs of consumers, available technologies, and identified risks. The paper proposes development scenarios and presents a model that takes them into account. Modeling results show that in all scenarios, up to 50% of the energy balance in 2035 will take gas, but the role of carbon-free energy sources will increase. The mathematical model allowed forecasting the demand for energy types by certain types of consumers, which makes it possible to determine the vector of development and stimulation of certain types of resources for energy production in the Arctic. The model enables considering not only the growth but also the decline in demand for certain types of consumers under different scenarios. In addition, authors’ forecasts, through further modernization of the energy sector in the Arctic region, can contribute to the creation of prerequisites that will be stimulating and profitable for the growth of investment in sustainable energy sources to supply consumers. The scientific significance of the work lies in the application of a consistent hybrid modeling approach to forecasting demand for energy resources in the Arctic region. The results of the study are useful in drafting a scenario of regional development, taking into account the Sustainable Development Goals, as well as identifying areas of technology and energy infrastructure stimulation.
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Veals, Peter G., W. James Steenburgh, and Leah S. Campbell. "Factors Affecting the Inland and Orographic Enhancement of Lake-Effect Precipitation over the Tug Hill Plateau." Monthly Weather Review 146, no. 6 (June 2018): 1745–62. http://dx.doi.org/10.1175/mwr-d-17-0385.1.
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The factors affecting the inland and orographic enhancement of lake-effect precipitation are poorly understood, yet critical for operational forecasting. Here we use nine cool seasons (16 November–15 April) of radar data from the Montague/Ft. Drum, New York (KTYX), WSR-88D, the North American Regional Reanalysis (NARR), and observations from the Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine variations in lake-effect precipitation enhancement east of Lake Ontario and over the Tug Hill Plateau (hereafter Tug Hill). Key factors affecting the inland and orographic enhancement in this region include the strength of the incident boundary layer flow, the intensity of the lake-induced convective available potential energy (LCAPE), and the mode of the lake-effect system. Stronger flow favors higher precipitation rates, a precipitation maximum displaced farther downwind, and greater inland and orographic enhancement. The effects of LCAPE depend upon the strength of the flow. During periods of weak flow, higher LCAPE favors lower precipitation rates, a maximum closer to the shoreline, and lesser inland and orographic enhancement. During periods of strong flow, higher LCAPE favors higher precipitation rates, a maximum displaced farther downwind, and greater inland and orographic enhancement. Banded (nonbanded) modes favor higher (lower) precipitation rates, lesser (greater) inland and orographic enhancement, and a maximum closer to the shoreline (over Tug Hill). These results, for both manually measured and radar-estimated precipitation, are robust when many lake-effect events are considered, but substantial variability exists during individual events.
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Abas, Azlan, Azmi Aziz, and Azahan Awang. "A Systematic Review on the Local Wisdom of Indigenous People in Nature Conservation." Sustainability 14, no. 6 (March 15, 2022): 3415. http://dx.doi.org/10.3390/su14063415.
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The local wisdom of indigenous people in nature conservation plays a critical part in protecting the planet’s biodiversity and the overall health of the ecosystems. However, at the same time, indigenous people and their lands are facing immense threats through modernization and globalization. This study aims to systematically review and analyze the local wisdom of the indigenous people in nature conservation. The present study integrated multiple research designs, and the review was based on the published standard, namely the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). This study used Web of Science (WoS) and Scopus as the main databases in searching for the required articles. Through content analysis, this study can be divided into seven main categories: (a) forest management, (b) flora and fauna conservation, (c) food security, (d) water management, (e) land management, (f) weather forecasting, and (g) others. The findings offer some basics on how academics can adopt and adapt the existing local wisdom of indigenous people in nature conservation into the scientific framework and design to answer the Sustainable Development 2030 Agenda.
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Kazemi Rad, Melissa, David Riley, Somayeh Asadi, and Parhum Delgoshaei. "Improving the performance profile of energy conservation measures at the Penn State University Park Campus." Engineering, Construction and Architectural Management 24, no. 4 (July 17, 2017): 610–28. http://dx.doi.org/10.1108/ecam-02-2016-0050.
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Purpose The purpose of this paper is to examine significant steps taken by the Pennsylvania State University (Penn State) to account for both energy cost savings and greenhouse gas (GHG) emissions reduction goals through strategic investments in energy conservation measures (ECMs) in campus buildings. Through an analysis of multiple years of investment in facility upgrades across the university, the impacts of ECMs of various types are characterized by building type. The standards and criteria for ECMs investments are also evaluated with the goal to develop a predictive tool to support decision making pertaining to an annual investment in a portfolio of ECMs that will maintain a trajectory to achieve both financial return on investment as well as GHG reduction goals. Design/methodology/approach This study is comprised of three main parts: analyzing the energy costs saving and GHG emissions reduction contribution of various building types in which ECMs were conducted, analyzing costs saving and GHG emissions reduction contribution of each ECM while considering the average annual investments made in them and estimating the impact of upgrading Penn State’s steam plants from firing a mixture of coal and natural gas to natural gas only on the GHG emissions. Findings These analyses help identify which types of buildings and ECMs would have larger savings and emissions reduction contributions. A calculator is also created to enable forecasting of costs saving and GHG emissions reduction of investment distribution strategy among ECMs. This study demonstrates that the calculator based on data from previous years will benefit decision makers in more wisely configuring the investment portfolio. Originality/value This paper fulfills an identical need to couple energy efficiency strategies coupled with the environmental impacts associated with different fossil fuel energy sources.
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Su, Moting, Zongyi Zhang, Ye Zhu, and Donglan Zha. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm." Energies 12, no. 6 (March 21, 2019): 1094. http://dx.doi.org/10.3390/en12061094.
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Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. The LSBoost method is adopted to analyze data series at daily, weekly and monthly. An empirical study verified that the proposed prediction model has a high degree of fitting. Compared with some existing approaches such as linear regression, linear support vector machine (SVM), quadratic SVM, and cubic SVM, the proposed LSBoost-based model showed better performance such as a higher R-square and lower mean absolute error, mean square error, and root-mean-square error.
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Niu, Dong Xiao, Yan Chao Chen, Jiao Fan, Qing Guo Ma, and Qin Liang Tan. "Study of Super Short-Term Bus Load Forecasting Model Based on Similar Ranges." Applied Mechanics and Materials 492 (January 2014): 482–88. http://dx.doi.org/10.4028/www.scientific.net/amm.492.482.
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With the increasing demand of electricity dispatching and the request of energy conservation and emission reduction, the dispatching plan and operating of electric power has become more and more important. On one hand, electric power companies try to reduce power reserve as much as possible to increase the efficiency; on the other hand, some power reserve is necessary to deal with emergency and to ensure the safety and stability of grid. Therefore, this paper proposes a super short-term bus load forecasting model which is based on similar ranges to track the variation of weather and load. By using the method of fruit flies optimizing grey neural network in the real time, it can reduce the size of the network computing and solve the problem of divergence. Since the system based on the model has operated for one year, it proves that this model can meet the requirement of the precision for the electricity dispatching and adapt to the changes in different regions.
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Lebo, Z. J., and J. H. Seinfeld. "A continuous spectral aerosol-droplet microphysics model." Atmospheric Chemistry and Physics Discussions 11, no. 8 (August 22, 2011): 23655–705. http://dx.doi.org/10.5194/acpd-11-23655-2011.
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Abstract. A two-dimensional (2-D) continuous spectral aerosol-droplet microphysics model is presented and implemented into the Weather Research and Forecasting (WRF) model for large-eddy simulations (LES) of warm marine stratocumulus clouds. Activation and regeneration of aerosols are treated explicitly in the calculation of condensation/evaporation. The model includes a 2-D spectrum that encompasses wet aerosol particles (i.e. haze droplets), cloud droplets, and drizzle droplets in a continuous and consistent manner and allows for the explicit tracking of aerosol size within cloud droplets due to collision-coalescence. The system of differential equations describing condensation/evaporation (i.e. mass conservation and energy conservation) is solved simultaneously within each grid cell. The model is demonstrated by simulating a marine stratocumulus deck for two different aerosol loadings (100 and 500 cm−3), and comparison with the more traditional microphysics modeling approaches (both 1-D bin and bulk schemes) is evaluated.
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Ndife, Alexander N., Wattanapong Rakwichian, Paisarn Muneesawang, and Yodthong Mensin. "Smart power consumption forecast model with optimized weighted average ensemble." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (September 1, 2022): 1004. http://dx.doi.org/10.11591/ijai.v11.i3.pp1004-1018.
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Smart power forecasting enables energy conservation and resource planning. Power estimation through previous utility bills is being replaced with machine intelligence. In this paper, a neural network architecture for demand side power consumption forecasting, called SGtechNet, is proposed. The forecast model applies ConvLSTM-encoder-decoder algorithm designed to enhance the quality of spatial encodings in the input feature to make a 7-day forecast. A weighted average ensemble approach was used, where multiple models were trained but only allow each model’s contribution to the prediction to be weighted proportionally to their level of trust and estimated performance. This model is most suitable for low-powered devices with low processing and storage capabilities like smartphones, tablets and iPads. The power consumption comparison between a manually operated home and a smart home was investigated and the model’s performance was tested on a time-domain household power consumption dataset and further validated using a real time load profile collated from the School of Renewable Energy and Smart Grid Technology, Naresuan University Smart Office. An improved root mean square error (RMSE) of 358 kwh was achieved when validated with holdout validation data from the automated office. Overall performance error, forecast and computational time showed a significant improvement over published research efforts identified in a literature review.