Abstract:In the context of the new normal, the global economy is entering a deep adjustment period, and the driving forces of development are also constantly changing. As a result, China’s economy has also entered a “new normal” phase in which it is growing in a manageable and relatively balanced manner. In addition, the new normal characteristics of the power industry’s development in China are also very significant, and they affect the adaptability of traditional power forecasting methods. By analyzing the new charac… Show more
“…More specifically, Beijing experienced severe air pollution during 2013 and 2014, and then PM2.5 dropped significantly during 2015 to 2020. After 2020, the level of PM2.5 in Beijing seemed the logistic model, chosen for its similarity to the observed "S" shape in PM2.5 data, estimates the probability of the dependent variable based on influencing independent variables (Cui & Zhao, 2019). The formula for the simple logistic model is as follows:…”
This paper examines Beijing air pollution treatment through examining the Aerosol Optical Depth from 2013 to 2019, and pollutants density change in the region of Beijing city, Tianjin city, and Hebei province, using remote sensing products, i.e., aerosol optical depth from MODIS and the Sentinel- 5P SO2 and NO2 density products in Google Earth Engine and QGIS. The study finds that the whole region experienced an AOD decrease. In addition, the patterns suggest that Beijing didn’t shift its pollution to the surrounding area. However, Beijing city’s SO2 and NO2 density still remain high in the region. Additionally, a machine learning model is introduced to further understand the dynamics of PM2.5 in Beijing.
“…More specifically, Beijing experienced severe air pollution during 2013 and 2014, and then PM2.5 dropped significantly during 2015 to 2020. After 2020, the level of PM2.5 in Beijing seemed the logistic model, chosen for its similarity to the observed "S" shape in PM2.5 data, estimates the probability of the dependent variable based on influencing independent variables (Cui & Zhao, 2019). The formula for the simple logistic model is as follows:…”
This paper examines Beijing air pollution treatment through examining the Aerosol Optical Depth from 2013 to 2019, and pollutants density change in the region of Beijing city, Tianjin city, and Hebei province, using remote sensing products, i.e., aerosol optical depth from MODIS and the Sentinel- 5P SO2 and NO2 density products in Google Earth Engine and QGIS. The study finds that the whole region experienced an AOD decrease. In addition, the patterns suggest that Beijing didn’t shift its pollution to the surrounding area. However, Beijing city’s SO2 and NO2 density still remain high in the region. Additionally, a machine learning model is introduced to further understand the dynamics of PM2.5 in Beijing.
“…Historic energy demand [17,27,30,[61][62][63]75,[84][85][86]91,96,118,131,133,134,138,140,143,146,[148][149][150]152,154,[156][157][158][159][160]168,181,189,192,202,230,231,234,239,240,242,243,255,262,263,268,270,273,277,280,282,…”
In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.
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