Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight

Yang, Dongchuan; Guo, Junkang; Li, Jie; Wang, Shouyang; Sun, Shaolong

doi:10.3389/fenrg.2021.771433

Cited by 9 publications

(3 citation statements)

References 49 publications

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“…Muhammad Zubair et al (2011) [11] presented a method for reliability parameters calculation and their updating in Probabilistic Safety Assessment. Dingqing Guo et al (2021) [12] presented a method for dynamic reliability evaluation of diesel generator system.…”

Section: Introductionmentioning

confidence: 99%

Development and application research of startup failure and operation failure model of emergency diesel generator in nuclear power plants

Yang,

Qing,

et al. 2023

J. Phys.: Conf. Ser.

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In order to fully grasp the law of startup failure and operation failure of emergency diesel generator in nuclear power plants, this paper first presents the development method of integrated fault model for emergency diesel generator startup failure and operation failure on the basis of fully integrating the existing mature fault tree analysis methods. Then, taking a certain type of Emergency diesel generator as an example, a complete startup failure and operation failure model was developed, and qualitative analysis and quantitative evaluation were carried out on the basis of the model, which verified the accuracy and applicability of the model development method. Finally, this paper puts forward the qualitative analysis and quantitative evaluation results based on model, which are directly used in the basic application scheme of equipment safeguard measures and the advanced application scheme of dynamic risk assessment and long-term trend monitoring of emergency diesel generator sets based on model + data driven by emergency diesel generator sets. Through the advanced application solution, users can identify dynamic high-risk defects and long-term reliability management weaknesses in advance, and formulate effective short-term fault elimination and long-term fault prevention measures in time to avoid the consequences of startup failure and operation failure. The above research results have important practical significance for the continuous improvement of the startup reliability and operation reliability of emergency diesel generator in nuclear power plants.

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Section: Introductionmentioning

confidence: 99%

Development and application research of startup failure and operation failure model of emergency diesel generator in nuclear power plants

Yang,

Qing,

et al. 2023

J. Phys.: Conf. Ser.

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“…Electricity demand forecasting becomes increasingly important (Yang et al, 2021) and distinguishing between different households in electricity consumption leads to more accurate results (Grandjean et al, 2012;Carlson et al, 2013). Tusting et al (2019) showed correlations between housing type and socio-economic factors by analyzing 51 national census reports in sub-Saharan Africa.…”

Section: Introductionmentioning

confidence: 99%

Bottom-up assessment of household electricity consumption in dynamic cities of the Global South—Evidence from Kigali, Rwanda

Vetter-Gindele

Bachofer

Braun

et al. 2023

Front. Sustain. Cities

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Data on electricity consumption is crucial for assessing and modeling energy systems, making it a key element of sustainable urban planning. However, many countries in the Global South struggle with a shortage of statistically valid, geocoded, and disaggregated household-level data. This paper aims to develop a generic methodology for the generation of such a database in terms of electricity consumption. The methodology was tested in Kigali, the capital city of Rwanda, with a focus on all single-family residential building types of the inner city. Discrete data on buildings is obtained through combined information products derived from very high resolution (VHR) satellite imagery, field surveys, and computer assisted personal interviewing. In total, 509 valid geocoded survey datasets were used to evaluate and model household electricity consumption, as well as electrical appliance ownership. The study's findings reveal that the arithmetic mean of specific electricity consumption was 3.66 kWh per household per day and 345 kWh per capita per year in 2015. By subdividing the data into distinct building types as well as their spatial location, and weighting the specific values according to their proportion in the study area, a more accurate mean value of 1.88 kWh per household per day and 160 kWh per capita per year was obtained. Applying this weighted mean to extrapolate household electricity consumption for the study area, in conjunction with the sample's precision level, resulted in an estimate of 126–137 GWh for the year 2015. In contrast, using the arithmetic mean would have led to values twice as high, even exceeding the total electricity consumption of the entire city, including multi-family and non-residential buildings. The study highlights the significance of on-site data collection combined with geospatial mapping techniques in enhancing of understanding of residential energy systems. Using building types as indicators to distinguish between households with contrasting electricity consumption and electrical appliance load levels can address the challenges posed by rapid urban growth in the Global South. This proposed method can assist municipal administrations in establishing a database that can be updated resource-efficiently at regular intervals by acquiring new satellite images.

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“…(2) Investigate the factors that have a significant effect on the electricity consumption in residential sectors. (3) Apply modern data science approaches, namely the seasonal statistical method (SARIMAX) and the machine learning algorithm (NARX), to forecast short-term electricity demand. (4) Find the best model by automatic tunning hyperparameters via the Bayesian optimization algorithm (BOA).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

et al. 2022

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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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Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight

Cited by 9 publications

References 49 publications

Development and application research of startup failure and operation failure model of emergency diesel generator in nuclear power plants

Development and application research of startup failure and operation failure model of emergency diesel generator in nuclear power plants

Bottom-up assessment of household electricity consumption in dynamic cities of the Global South—Evidence from Kigali, Rwanda

Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

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