Short Term Forecasting(短期预测)研究综述
Short Term Forecasting 短期预测 - The accurate short-term forecasting of power outputs is essential to the extensive integration of wind generation into power grids. [1] Short-term forecasting provides predictions up to 7 days ahead. [2] Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. [3] Conclusion Due to limited data availability during the ongoing pandemic, less data-hungry short-term forecasting models, like ARIMA and ETS, can help in anticipating the future outbreaks of SARS-CoV2. [4] In this paper, the short-term forecasting of landslides (STFL) model is proposed by analyzing landslide deformation characteristics in a known evolution process. [5] For a long time, market forecasting of the construction machinery is regarded as short-term forecasting, which lacks the analysis of macro-marketing law and cannot reflect the true law of market development. [6] Stimulated by current developments in deep learning (DL) techniques as well as the promising efficiency in energy-related applications, this study introduces a novel DL architecture, called PV-Net, for short-term forecasting of day-ahead PV energy. [7] It is, thus, considered to be appropriate for use in short-term forecasting over large areas. [8] The proposed neural network technology for predicting TS yield levels using a pre-forecast autocorrelation analysis of retrospective levels reduces the error of short-term forecasting of grain yield in the arid natural and climatic conditions of the Lower Volga region. [9] Emphasis is placed on the fact that adaptive models based on ostentatious smoothing of time series should be used to optimize the logistics flows of the organization as the main tool for short-term forecasting. [10] The method can also be used for other kinds of short-term forecasting. [11] This study proposes a novel approach of using genetically optimized non-linear auto-regressive recurrent neural networks (NARX) for ultra-short-term forecasting of PV power output. [12] The performance of load forecast for Long Island region taking GT and weather data as input variables is compared with that taking only weather data as input variables, which shows that the introduction of GT improves short-term forecasting effectiveness significantly. [13] Due to the impact of many local sudden changes in photovoltaic (PV) output power, its ultra-short-term forecasting is facing great challenges. [14] With the growing dependence on wind power generation, improving the accuracy of short-term forecasting has become increasingly important for ensuring continued economical and reliable system operations. [15] Our result shows that the Conv-LSTM neutral network can train the model with a reasonable output and is suitable for short-term forecasting network ride-hailing demand forecast with spatiotemporal feature information. [16] The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. [17] Accurate short-term forecasting of photovoltaic (PV) power is indispensable for controlling and designing smart energy management systems for microgrids. [18] Accurate short-term forecasting of solar radiation can ensure the safety of photovoltaic grids and improve the utilization efficiency of the solar energy systems. [19] In addition, based on the forecasting results of the VAR (1)-GARCH (1,1) model, it shows that this model is good for short-term forecasting. [20] In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. [21] This paper proposes a new model for short-term forecasting power generation capacity of large-scale solar power plant (SPP) in Vietnam considering the fluctuations of weather factors when applying the Long Short-Term Memory networks (LSTM) algorithm. [22] This paper focused on short-term forecasting of high-frequency global horizontal irradiance data from one of South Africa’s radiometric stations. [23] Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). [24] Nowcasting, or the short-term forecasting of precipitation, is urgently needed to support the mitigation circle in hydrometeorological disasters. [25] Implications for coupled data assimilation and short-term forecasting are discussed. [26] Short-term forecasting of the gross product of the regional economy is carried out using a production function that provides a better approximation of retrospective data of the time interval of the previous forecasting year. [27] The paper presents a developed methodology of short-term forecasting for heat production in combined heat and power (CHP) plants using a big data-driven model. [28] Introduction: The novel coronavirus (COVID-19) has significantly spread over the world and impacted with new challenges to the research community Although governments initiated numerous containment and social distancing measures all over the world, the need for healthcare resources has dramatically increased and the effective management of infected patients becomes a challenging task for healthcare centres Objective: Thus, the objective of the research is to find the accurate short-term forecasting of the number of new confirmed covid-19 positive cases is important for optimizing the available resources and slowing down the progression of COVID-19 Recently, various methods like machine learning models and other algorithms demonstrated important improvements when handling time-series data in various applications Methods: This paper presents a comparative study of different machine learning methods and models to forecast the number of new cases Specifically, Long short-term memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Holt’s Linear forecasting model, Exponential smoothing and Moving-average model algorithms have been applied for forecasting of COVID-19 cases based on data set Result: Results were analysed using various parameters like Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, Error Vector Magnitude Root Mean Square Logarithmic Error Conclusion: As a conclusion, compared to other models, Long Short TermModel predicted better forecasting and gives the best performance in terms of different parameters © IJCRR. [29] Short-term forecasting methods can be roughly classified into physics-based modelling and data-based modelling. [30] Two proposed time series decomposition methods are developed for short-term forecasting of the CO2 emissions of electricity. [31] Firstly, based on the strong correlation between PWV_Met and ZTD hourly sequences from the International GNSS Service Network’s BJFS station for DOYS 182-212, 2015, the results of experiment prove that the reliability of GNSS ZTD is used to forecast PWV_Met in short-term forecasting. [32] The natural gas energy prediction results show that this method has high prediction accuracy compared with other methods, which means that the method proposed in this paper can be used as an effective tool for short-term forecasting of natural gas production in the United States and play an auxiliary role in energy forecasting. [33] Short-term forecasting is often used to predict with calculations using time, hour, day or week. [34] The paper considers short-term forecasting of the intensity of solar radiation in the city of Odessa based on an artificial neural network. [35] long-short-term memory and recurrent neural networks combination is formulated for very short-term forecasting of wind speed and solar radiation. [36] Accurate short-term forecasting is thus vital to support country-level policy making. [37] In view of the fact that the existing ultra-short-term forecasting method of wind power is difficult to effectively mine and analyse the inherent variation law of data, a forecasting method of wind power based on wavelet decomposition combined with bi- directional long-short term memory neural network (W-BiLSTM) is proposed. [38] Thus, short-term forecasting has crucial utility for generating dispatching commands, managing the spot market, and detecting anomalies. [39] The article considers a variant of inclusion of a neural network into the microwave radiometric system of remote sensing of the atmosphere to perform short-term forecasting of meteorological parameters, based on current measurements of power of own radio-thermal emission of the atmosphere in three frequency bands. [40] The inefficiency of short-term forecasting of strong earthquakes is obvious. [41] Furthermore, we conduct additional experiments on two other cryptocurrencies, Ethereum and Litecoin, to further confirm the effectiveness of the MRCLSTM in short-term forecasting for multivariate time series of cryptocurrencies. [42] According to the predicted time span, the power load forecasting problem can be roughly divided into long-term and short-term forecasting. [43] In this study, the soft computing method is used for short-term forecasting of agriculture commodity price based on time series data using the artificial neural network (ANN). [44] Two short-term forecasting models based on non-parametric kernel density estimation (KDE) and mixture density networks (MDN) are developed that estimate the conditional probability of the customers' aggregate demand at any given temperature and time. [45] The first approach is physically based and adapts the solar short-term forecasting approach referred to as “smart-persistence” to wind power forecasting. [46] This paper presents the tracking and short-term forecasting of mesoscale convective cloud clusters (CCs) that occurred over southeast Brazil and the adjacent Atlantic Ocean during 2009–17. [47] This article intends to provide short-term forecasting of WWTP energy consumption based on data-driven soft sensors using traditional time-series and deep learning methods. [48] The time series model with polynomial smoothing is used for short-term forecasting that allows analysing the dynamics of passenger traffic, decreasing costs on monitoring of information on passenger traffic and allows using results for solving tasks of technological organization of public transport. [49] In contrast to short-term forecasting, it is often a onetime exercise and forecasts are rarely generated continuously. [50]准确的短期电力输出预测对于将风力发电广泛并入电网至关重要。 [1] 短期预测提供最多提前 7 天的预测。 [2] 由于开发 COVID-19 疫苗的时间要求，需要证据来为县、卫生区和州级别的短期预测方法选择提供信息。 [3] 结论由于在持续的大流行期间数据可用性有限，需要较少数据的短期预测模型（如 ARIMA 和 ETS）可以帮助预测未来 SARS-CoV2 的爆发。 [4] 本文通过分析已知演化过程中的滑坡变形特征，提出了滑坡短期预测（STFL）模型。 [5] 长期以来，工程机械市场预测被视为短期预测，缺乏对宏观市场规律的分析，不能反映市场发展的真实规律。 [6] 在深度学习 (DL) 技术的当前发展以及能源相关应用的高效率的刺激下，本研究引入了一种称为 PV-Net 的新型 DL 架构，用于对日前光伏能源进行短期预测。 [7] 因此，它被认为适用于大面积的短期预测。 [8] 所提出的使用回顾性水平的预预测自相关分析预测TS产量水平的神经网络技术减少了伏尔加河下游地区干旱自然和气候条件下粮食产量的短期预测误差。 [9] 重点放在一个事实，即基于时间序列的炫耀平滑的自适应模型应该被用来优化组织的物流流程，作为短期预测的主要工具。 [10] 该方法也可用于其他类型的短期预测。 [11] 本研究提出了一种使用遗传优化的非线性自回归递归神经网络 (NARX) 对光伏功率输出进行超短期预测的新方法。 [12] 比较了以GT和天气数据为输入变量的长岛地区负荷预测与仅以天气数据为输入变量的负荷预测性能，表明GT的引入显着提高了短期预测的有效性。 [13] 受多地光伏（PV）输出功率突变影响，其超短期预测面临巨大挑战。 [14] 随着对风力发电的依赖日益增加，提高短期预测的准确性对于确保持续经济和可靠的系统运行变得越来越重要。 [15] 我们的结果表明，Conv-LSTM中性网络能够以合理的输出训练模型，适用于具有时空特征信息的短期预测网络乘车需求预测。 [16] 该模型以 Prophet 和季节性幼稚模型为基准，表明当前模型在非常短期的预测中更加熟练和可靠。 [17] 光伏 (PV) 功率的准确短期预测对于控制和设计微电网的智能能源管理系统是必不可少的。 [18] 准确的太阳辐射短期预报可以保证光伏电网的安全，提高太阳能系统的利用效率。 [19] 此外，基于VAR(1)-GARCH(1,1)模型的预测结果，表明该模型有利于短期预测。 [20] 本研究评估了闪电资料同化在准噶尔盆地西缘和杰尔山东部一次中等降水事件的短期预报中的表现。 [21] 本文提出了一种在应用长短期记忆网络（LSTM）算法时考虑天气因素波动的越南大型太阳能发电厂（SPP）短期预测发电能力的新模型。 [22] 本文重点关注来自南非一个辐射测量站的高频全球水平辐照度数据的短期预测。 [23] 在这里，我们报告了德国和波兰（2020 年 10 月 12 日至 12 月 19 日）对 COVID-19 的十周协作短期预测的见解。 [24] 迫切需要临近预报或降水的短期预报，以支持水文气象灾害的减缓循环。 [25] 讨论了耦合数据同化和短期预测的影响。 [26] 区域经济生产总值的短期预测是使用生产函数进行的，该函数提供了对上一个预测年时间间隔的追溯数据的更好近似。 [27] 本文介绍了一种使用大数据驱动模型对热电联产 (CHP) 工厂的热量生产进行短期预测的成熟方法。 [28] 简介：新型冠状病毒 (COVID-19) 已在全球范围内广泛传播，并给研究界带来了新的挑战尽管各国政府在世界各地采取了许多遏制和社会疏离措施，但对医疗资源的需求急剧增加，有效的感染患者的管理成为医疗中心的一项艰巨任务目标：因此，研究的目的是找到对新确诊的 covid-19 阳性病例数量的准确短期预测，这对于优化可用资源和减慢速度很重要COVID-19 的进展最近，机器学习模型和其他算法等各种方法在各种应用中处理时间序列数据时表现出重要的改进方法：本文介绍了不同机器学习方法和模型的比较研究，以预测新的数量具体来说，长期短期记忆（LSTM） , 自回归综合移动平均 (ARIMA), Holt 的线性预测模型, 指数平滑和移动平均模型算法已应用于基于数据集的 COVID-19 病例预测结果: 使用各种参数分析结果, 如均方根误差,平均绝对误差、平均绝对百分比误差、误差向量幅度根均方对数误差结论：作为结论，与其他模型相比，Long Short TermModel 预测更好，并在不同参数方面提供最佳性能 © IJCRR。 [29] 短期预测方法大致可分为基于物理的建模和基于数据的建模。 [30] 为短期预测电力的二氧化碳排放量开发了两种建议的时间序列分解方法。 [31] 首先，基于国际 GNSS 服务网 BJFS 站 DOYS 182-212, 2015 的 PWV_Met 与 ZTD 小时序列之间的强相关性，实验结果证明 GNSS ZTD 用于短期预测 PWV_Met 的可靠性. [32] 天然气能量预测结果表明，该方法与其他方法相比具有较高的预测精度，这意味着本文提出的方法可以作为美国天然气产量短期预测的有效工具，并发挥在能源预测中起到辅助作用。 [33] 短期预测通常用于通过使用时间、小时、天或周的计算进行预测。 [34] 本文考虑基于人工神经网络对敖德萨市太阳辐射强度的短期预测。 [35] 长短期记忆和循环神经网络组合用于风速和太阳辐射的极短期预测。 [36] 因此，准确的短期预测对于支持国家层面的政策制定至关重要。 [37] 针对现有风电超短期预测方法难以有效挖掘和分析数据内在变化规律的问题，提出一种基于小波分解结合双向长短线的风电预测方法。提出了术语记忆神经网络（W-BiLSTM）。 [38] 因此，短期预测对于生成调度命令、管理现货市场和检测异常具有至关重要的作用。 [39] 本文考虑了一种在大气遥感微波辐射测量系统中加入神经网络的变体，以根据当前对大气自身辐射热发射功率的三个频率测量值进行气象参数的短期预测。乐队。 [40] 强震短期预报的低效性是显而易见的。 [41] 此外，我们对另外两种加密货币 Ethereum 和 Litecoin 进行了额外的实验，以进一步确认 MRCLSTM 在加密货币多变量时间序列的短期预测中的有效性。 [42] 根据预测的时间跨度，电力负荷预测问题大致可分为长期预测和短期预测。 [43] 本研究利用人工神经网络（ANN）基于时间序列数据，采用软计算方法对农产品价格进行短期预测。 [44] 开发了两个基于非参数核密度估计（KDE）和混合密度网络（MDN）的短期预测模型，用于估计在任何给定温度和时间下客户总需求的条件概率。 [45] 第一种方法是基于物理的，并将称为“智能持久性”的太阳能短期预测方法应用于风能预测。 [46] 本文介绍了 2009-17 年间发生在巴西东南部和邻近大西洋上空的中尺度对流云团 (CC) 的跟踪和短期预报。 [47] 本文旨在使用传统的时间序列和深度学习方法，基于数据驱动的软传感器提供污水处理厂能耗的短期预测。 [48] 具有多项式平滑的时间序列模型用于短期预测，可以分析客运量的动态，降低监控客运量信息的成本，并允许将结果用于解决公共交通技术组织的任务。 [49] 与短期预测相比，它通常是一次性的，很少连续生成预测。 [50]
long short term
This paper proposes a new model for short-term forecasting power generation capacity of large-scale solar power plant (SPP) in Vietnam considering the fluctuations of weather factors when applying the Long Short-Term Memory networks (LSTM) algorithm. [1] Introduction: The novel coronavirus (COVID-19) has significantly spread over the world and impacted with new challenges to the research community Although governments initiated numerous containment and social distancing measures all over the world, the need for healthcare resources has dramatically increased and the effective management of infected patients becomes a challenging task for healthcare centres Objective: Thus, the objective of the research is to find the accurate short-term forecasting of the number of new confirmed covid-19 positive cases is important for optimizing the available resources and slowing down the progression of COVID-19 Recently, various methods like machine learning models and other algorithms demonstrated important improvements when handling time-series data in various applications Methods: This paper presents a comparative study of different machine learning methods and models to forecast the number of new cases Specifically, Long short-term memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Holt’s Linear forecasting model, Exponential smoothing and Moving-average model algorithms have been applied for forecasting of COVID-19 cases based on data set Result: Results were analysed using various parameters like Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, Error Vector Magnitude Root Mean Square Logarithmic Error Conclusion: As a conclusion, compared to other models, Long Short TermModel predicted better forecasting and gives the best performance in terms of different parameters © IJCRR. [2] long-short-term memory and recurrent neural networks combination is formulated for very short-term forecasting of wind speed and solar radiation. [3] In view of the fact that the existing ultra-short-term forecasting method of wind power is difficult to effectively mine and analyse the inherent variation law of data, a forecasting method of wind power based on wavelet decomposition combined with bi- directional long-short term memory neural network (W-BiLSTM) is proposed. [4] The work is aimed at identifying the most effective methodology for short-term forecasting of energy consumption concerning intervals of 1 hour to 1 week based on the employment of im-proved fuzzy recurrence and long short-term memory neural networks. [5] Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid. [6]本文提出了一种在应用长短期记忆网络（LSTM）算法时考虑天气因素波动的越南大型太阳能发电厂（SPP）短期预测发电能力的新模型。 [1] 简介：新型冠状病毒 (COVID-19) 已在全球范围内广泛传播，并给研究界带来了新的挑战尽管各国政府在世界各地采取了许多遏制和社会疏离措施，但对医疗资源的需求急剧增加，有效的感染患者的管理成为医疗中心的一项艰巨任务目标：因此，研究的目的是找到对新确诊的 covid-19 阳性病例数量的准确短期预测，这对于优化可用资源和减慢速度很重要COVID-19 的进展最近，机器学习模型和其他算法等各种方法在各种应用中处理时间序列数据时表现出重要的改进方法：本文介绍了不同机器学习方法和模型的比较研究，以预测新的数量具体来说，长期短期记忆（LSTM） , 自回归综合移动平均 (ARIMA), Holt 的线性预测模型, 指数平滑和移动平均模型算法已应用于基于数据集的 COVID-19 病例预测结果: 使用各种参数分析结果, 如均方根误差,平均绝对误差、平均绝对百分比误差、误差向量幅度根均方对数误差结论：作为结论，与其他模型相比，Long Short TermModel 预测更好，并在不同参数方面提供最佳性能 © IJCRR。 [2] 长短期记忆和循环神经网络组合用于风速和太阳辐射的极短期预测。 [3] 针对现有风电超短期预测方法难以有效挖掘和分析数据内在变化规律的问题，提出一种基于小波分解结合双向长短线的风电预测方法。提出了术语记忆神经网络（W-BiLSTM）。 [4] nan [5] nan [6]
artificial neural network
The paper considers short-term forecasting of the intensity of solar radiation in the city of Odessa based on an artificial neural network. [1] In this study, the soft computing method is used for short-term forecasting of agriculture commodity price based on time series data using the artificial neural network (ANN). [2] In this paper, an artificial neural network (ANN)-based model is investigated for short-term forecasting of the hourly wind speed, solar radiation, and electrical power demand. [3] In this work, a new hybrid methodology (CANGENFIS) combining Multiple input -Multiple output, fuzzy logic, artificial neural networks and multiobjective genetic algorithms was developed to model farmer behaviour and short-term forecasting the distribution by tariff period of the irrigation depth applied at farm level. [4]本文考虑基于人工神经网络对敖德萨市太阳辐射强度的短期预测。 [1] 本研究利用人工神经网络（ANN）基于时间序列数据，采用软计算方法对农产品价格进行短期预测。 [2] nan [3] nan [4]
long term forecasting
While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variables in long terms. [1] Results show that our approach provides approximate performance in short-term forecasting and better performance in long-term forecasting. [2] Through short-term forecasting and long-term forecasting, the wave bending moment and wave force of each cross section of the LNG ship, as well as the motion response of LNG ship which have the greatest influence on the wave load are obtained. [3]虽然非常短期和短期的预测通常用点估计来表示，但由于长期天气变量等预测变量的固有不确定性，这种方法在中期和长期预测中非常不可靠。 [1] 结果表明，我们的方法在短期预测中提供了近似性能，在长期预测中提供了更好的性能。 [2] nan [3]
95 % prediction
COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases In this study, we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases, deaths, and recoveries in Pakistan for the upcoming month until the end of July For the decomposition of data, the Ensemble Empirical Mode Decomposition (EEMD) technique is applied EEMD decomposes the data into small components, called Intrinsic Mode Functions (IMFs) For individual IMFs modelling, we use the Autoregressive Integrated Moving Average (ARIMA) model The data used in this study is obtained from the official website of Pakistan that is publicly available and designated for COVID-19 outbreak with daily updates Our analyses reveal that the number of recoveries, new cases, and deaths are increasing in Pakistan exponentially Based on the selected EEMD-ARIMA model, the new confirmed cases are expected to rise from 213,470 to 311,454 by 31 July 2020, which is an increase of almost 1 46 times with a 95% prediction interval of 246,529 to 376,379 The 95% prediction interval for recovery is 162,414 to 224,579, with an increase of almost two times in total from 100802 to 193495 by 31 July 2020 On the other hand, the deaths are expected to increase from 4395 to 6751, which is almost 1 54 times, with a 95% prediction interval of 5617 to 7885 Thus, the COVID-19 forecasting results of Pakistan are alarming for the next month until 31 July 2020 They also confirm that the EEMD-ARIMA model is useful for the short-term forecasting of COVID-19, and that it is capable of keeping track of the real COVID-19 data in nearly all scenarios The decomposition and ensemble strategy can be useful to help decision-makers in developing short-term strategies about the current number of disease occurrences until an appropriate vaccine is developed. [1]COVID-19 已导致严重的健康并发症，并对全球经济产生重大不利影响 预测 COVID-19 感染的趋势有助于执行政策以有效减少新病例的数量 在本研究中，我们应用分解和集成模型预测巴基斯坦下个月至 7 月底的 COVID-19 确诊病例、死亡人数和康复情况 对于数据的分解，应用了整体经验模式分解 (EEMD) 技术 EEMD 将数据分解为小组件，称为内在模态函数 (IMF) 对于单个 IMF 建模，我们使用自回归综合移动平均 (ARIMA) 模型 本研究中使用的数据来自巴基斯坦官方网站，该官方网站可公开获取并指定用于 COVID-19 爆发并每日更新我们分析表明，巴基斯坦的康复人数、新病例和死亡人数呈指数增长。 EEMD-ARIMA 模型，预计到 2020 年 7 月 31 日，新增确诊病例将从 213,470 上升至 311,454，增加了近 1 46 倍，95% 预测区间为 246,529 至 376,379 95% 预测区间为 162,414到 224,579 人，到 2020 年 7 月 31 日，从 100802 人增加到 193495 人，总共增加了近两倍；另一方面，预计死亡人数将从 4395 人增加到 6751 人，几乎是 1 54 倍，预测区间为 95% 5617 至 7885 因此，巴基斯坦的 COVID-19 预测结果在 2020 年 7 月 31 日之前的下个月令人担忧 他们还证实 EEMD-ARIMA 模型对 COVID-19 的短期预测有用，并且能够在几乎所有情况下跟踪真实的 COVID-19 数据分解和集成策略有助于帮助决策者制定有关当前疾病发生次数的短期策略，直到开发出合适的疫苗。 [1]