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Background: Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects. Objectives: We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. Methods: We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. Results: Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV0–7 (0.9 °C). An IQR increase in inter-day TV0–7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV0–7 and inter-day TV0–7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. Conclusions: Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health.

This article proposes a framework to model the mutual volatility transmission between multiple assets and multiple trading places in different time zones. The model is estimated using a dataset containing daily returns of three stock indices – the MSCI Japan, the EuroStoxx 50, and the S&P 500 – each traded at three major trading places: the stock exchanges in Tokyo, London, and New York. Strong volatility transmission effects can be observed between New York and Tokyo, whereas current volatility in New York mostly depends on past volatility in New York. For the assets in consideration, spillovers are strong across trading zones, but weak across assets, suggesting a close connection between market places but only a loose volatility link between international stock indices. Volatility impulse response functions indicate a long-lasting and comparably large response of volatility in Tokyo, whereas they suggest a quicker volatility decay in London and New York.

Continuous increase of the production of photovoltaic energy requires precise forecasting to ensure grid stability. This paper presents a detailed benchmark of eight commercial forecasting solutions for intra-day solar forecasts. The comparison was carried out on a period of six months, from November to May 2021, on seven different PV plants located in different countries of the northern hemisphere. Performance evaluation metrics MAE, RMSE and MBE are used in order to analyze the forecasting precision. It is shown that forecasting solar power remains challenging, as shown by the important dispersion between the actors that we have observed. [ABSTRACT FROM AUTHOR]

Summary: Background: A growing number of studies have reported an increased risk of cardiovascular disease (CVD) and respiratory disease (RD) within hours after exposure to ambient air pollution or temperature. We assemble published evidence on the sub-daily associations of CVD and RD with ambient air pollution and temperature. Methods: Databases of PubMed and Web of Science were searched for original case-crossover and time-series designs of English articles examining the intra-day effects of ambient air pollution [particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5), ≤10 μm (PM10), 2.5–10μm (PM10-2.5), and < 7 μm (SPM), O3, SO2, NO2, CO, and NO] and temperatures (heat and cold) on cardiorespiratory diseases within 24 h after exposure in the general population by comparing with exposure at different exposure levels or periods. Meta-analyses were conducted to pool excess risks (ERs, absolute percentage increase in risk) of CVD and RD morbidities associated with an increase of 10 μg/m3 in particulate matters, 0.1 ppm in CO, and 10 ppb in other gaseous pollutants. Findings: Final analysis included thirty-three papers from North America, Europe, Oceania, and Asia. Meta-analysis found an increased risk of total CVD morbidity within 3 h after exposure to PM2.5 [ER%: 2.65% (95% CI: 1.00% to 4.34%)], PM10-2.5 [0.31% (0.02% to 0.59%)], O3 [1.42% (0.14% to 2.73%)], and CO [0.41% (0.01% to 0.81%)]. The risk of total RD morbidity elevated at lag 7–12 h after exposure to PM2.5 [0.69% (0.14% to 1.24%)] and PM10 [0.38% (0.02% to 0.73%)] and at lag 12–24 h after exposure to SO2 [2.68% (0.94% to 4.44%)]. Cause-specific CVD analysis observed an increased risk of myocardial infarction morbidity within 6 h after exposure to PM2.5, PM10, and NO2, and an increased risk of out-of-hospital cardiac arrest morbidity within 12 h after exposure to CO. Risk of total CVD also increased within 24 h after exposure to heat. Interpretation: This study supports a sudden risk increase of cardiorespiratory diseases within a few hours after exposure to air pollution or heat, and some acute and highly lethal diseases such as myocardial infarction and cardiac arrest could be affected within a shorter time. Funding: The National Natural Science Foundation of China (Grant No. 42105165; 81773518), the High-level Scientific Research Foundation of Anhui Medical University (Grant No. 0305044201), and the Discipline Construction of Anhui Medical University (Grant No. 0301001836).

• Two novel metrics were used to as indicators for intra-day TV and inter-day TV. • Two indicators are associated with increased mortality risk. • The association varied by climate areas, seasons, and Köppen–Geiger climate types. Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects. We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV 0–7 (0.9 °C). An IQR increase in inter-day TV 0–7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV 0–7 and inter-day TV 0–7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health. [ABSTRACT FROM AUTHOR]

For infants in the first months of life, breast milk is a complete source of nutrition; however, it can also contain elements that are harmful to the infant. It is therefore critical for infant health to characterize breast milk. The aim of this study was to determine the intra- and inter-day variation of elements in breast milk, for which there is currently limited information, as a pilot study for a larger study. Firstly, we developed a simple and robust analytical method for the determination of multiple elements in breast milk. It was accurate (accuracy ranged from 98% to 107%) for measurement of 26 elements in breast milk by quadrupole inductively coupled plasma-mass spectrometry. Intra- and inter-day variation of elements, protein, and fat in breast milk was determined by analyzing breast milk collected from 11 women at 12 sampling points over three days and calculating intraclass correlation coefficients. Intraclass correlation coefficients showed that while some elements were consistent across time points (e.g., Sr, Ca, and Cu), others showed very high variability (e.g., As, Cd, and Ni). Correlation analyses between elements in breast milk showed strong relationships between those including Fe and Mo, Ca and Sr, and Cd and Fe. [ABSTRACT FROM AUTHOR]

Intra-day stock market forecasting is one of the challenging tasks as its nature is difficult to predict due to the non-volatility of the stock market. Therefore, the building block concepts of machine learning techniques with the recurrent neural network have been used. These techniques are very supportive for developing the long short-term memory (LSTM) network. This network is mainly used for sequential learning while performing classification. This paper presents research on applied computing to assess the efficacy of a multi-level classifier over single classifier. The multi-level classifier is hybrid of machine learning techniques (logistic regression classifier, decision tree, support vector machine) and recurrent neural network (LSTM) aiming to maximize the predictive ability, increases the interpretability, minimizes the complexity, reduced the training time, and provides the generalized platform with an optimistic forecasting model. The developed model has the potential to act as an intelligent system with the ability to adapt the market behavior present in the dataset which analyzes the best performing feature subset based on model accuracy. The comparative results clearly reveal that the blended computation of multi-level classifiers outperforms the existing works and it also leads to more precise model development with the maximum predictive ability, approximately 10–12% increment in accuracy. [ABSTRACT FROM AUTHOR]

For infants in the first months of life, breast milk is a complete source of nutrition; however, it can also contain elements that are harmful to the infant. It is therefore critical for infant health to characterize breast milk. The aim of this study was to determine the intra- and inter-day variation of elements in breast milk, for which there is currently limited information, as a pilot study for a larger study. Firstly, we developed a simple and robust analytical method for the determination of multiple elements in breast milk. It was accurate (accuracy ranged from 98% to 107%) for measurement of 26 elements in breast milk by quadrupole inductively coupled plasma-mass spectrometry. Intra- and inter-day variation of elements, protein, and fat in breast milk was determined by analyzing breast milk collected from 11 women at 12 sampling points over three days and calculating intraclass correlation coefficients. Intraclass correlation coefficients showed that while some elements were consistent across time points (e.g., Sr, Ca, and Cu), others showed very high variability (e.g., As, Cd, and Ni). Correlation analyses between elements in breast milk showed strong relationships between those including Fe and Mo, Ca and Sr, and Cd and Fe.

This paper focuses on the modeling of the intra-day transactions at the Stock Exchange Mauritius (SEM) of the two major banking companies: Mauritius Commercial Bank Group Limited (MCB) and State Bank of Mauritius Holdings Ltd (SBMH) in Mauritius using a flexible non-stationary bivariate integer-valued moving average of order 1 (BINMA(1)) process with negative binomial (NB) innovations that may cater for different levels of over-dispersion. The generalized quasi-likelihood (GQL) approach is used to estimate the regression, dependence and over-dispersion effects. However, for the over-dispersion parameters, the auto-covariance structure in the GQL is constructed using some higher order moments. This new model is tested over some Monte-Carlo experiments and is applied to analyze the inter-related intra-day series of volume of stocks for the two banking institutions using data collected from 3 August to 16 October 2015 in the presence of some time-varying covariates such as the news effect, Friday effect and time of the day effect. [ABSTRACT FROM AUTHOR]

A long-standing literature documents intra-industry capital market co-movements around earnings releases, yet the dynamics of these information transfers remain largely unexplored. We provide evidence on both the sources and channels of information transfers by separating two distinct events within the reporting window using intra-day data and by exploring potential mechanisms of information flows. We document that the co-movement of absolute and signed stock returns over the conference call windows of announcing firms and their industry peers are statistically and economically larger than the co-movement over the corresponding earnings announcement windows. Turning to mechanisms, we find that shared analyst coverage, coverage by analysts providing industry recommendations, shared institutional ownership, and joint financial media mentions are each individually and incrementally associated with higher rate of information transfer over both the earnings announcement and conference call windows. Textual analyses reveal that peer mentions and macroeconomic discussions both significantly contribute to conference call information transfers. [ABSTRACT FROM AUTHOR]

A precise, sensitive and quick High Performance Liquid Chromatographic (HPLC) method for the determination of rosuvastatin calcium in bulk and tablet dosage forms has been validated. The chromatographic scheme involved: Sil-20A auto sampler, LC-20A pump, SPD-20A UV/visible detector with separation attained by C18 column at 40°C temperature through a mobile phase of acetonitrile and buffer (50:50) at a flow rate of 1.0ml/min. The method is precise (%RSD for intra-day and inter-day extended between 1.06-1.54% and 0.103-1.78%) and linear (r2=0.9997). Limit of detection and quantification (LOD & LOQ) of the adopted method were 0.78 and 1.56μg/ml. The proposed HPLC method was established to be sensitive, precise and swift that can be proficiently adopted in quality control/quality assurance laboratories for predictable investigation of the bulk and oral solid dosage forms of rosuvastatin calcium. [ABSTRACT FROM AUTHOR]

This paper explores the statistical and economical significance of intra-day and -week patterns in bid-ask spreads. We investigate a large panel of high frequency data for stocks traded on the XETRA trading platform and observe significant patterns in spreads. In addition to showing the robustness of our findings over time, as well as in cross-section, we are also able to demonstrate the patterns’ predictability in an out-of-sample approach. Our findings have clear implications, especially for uninformed but discretionary liquidity traders, which allow significant and economically relevant reductions of transaction costs. [ABSTRACT FROM AUTHOR]

We propose a bivariate integer-valued fractional integrated (BINFIMA) model to account for the long-memory property and apply the model to high-frequency stock transaction data. The BINFIMA model allows for both positive and negative correlations between the counts. The unconditional and conditional first- and second-order moments are given. The model is capable of capturing the covariance between and within intra-day time series of high-frequency transaction data due to macroeconomic news and news related to a specific stock. Empirically, it is found that Ericsson B has mean recursive process while AstraZeneca has long-memory property. [ABSTRACT FROM PUBLISHER]

In order to better handle the new features that emerge at both ends of supply and demand, new measures are constantly being introduced, such as demand-side management (DSM) and prediction of uncertain output and load. However, the existing DSM strategies, like real-time price (RTP), and dispatch methods are optimized separately, and response models of active loads, such as the interruptible load (IL), are still imperfect, which make it difficult for the active distribution network (ADN) to achieve global optimal operation. Therefore, to better manage active loads, the response characteristics including both the response time and the responsibility and compensation model of IL for cluster users, and the real-time demand response model for price based load, were analyzed and established. Then, a collaborative optimization strategy of RTP and optimal dispatch of ADN was proposed, which can realize an economical operation based on mutual benefit and win-win mode of supply and demand sides. Finally, the day-ahead and intra-day integrative dispatch model using different time-scale prediction data was established, which can achieve longer-term optimization while reducing the impact of prediction errors on the dispatch results. With numerical simulations, the effectiveness and superiority of the proposed strategy were verified.

Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized volatility. The forecasting evaluation is valid for standardized forecast errors with leptokurtic distribution as well as with leptokurtic and asymmetric distributions. Additionally, the widely applied forecasting evaluation function, the predicted mean-squared error, fails to select the adequate model in the case of models with residuals that are leptokurtically and asymmetrically distributed. Hence, the realized volatility forecasting evaluation should be based on the standardized forecast errors instead of their unstandardized version. [ABSTRACT FROM AUTHOR]

A model to account for the long-memory property in a count data framework is proposed and applied to high-frequency stock transactions data. By combining features of the INARMA and ARFIMA models, an Integer-valued Auto Regressive Fractionally Integrated Moving Average (INARFIMA) model is proposed. The unconditional and conditional first- and second-order moments are given. The CLS, FGLS and GMM estimators are discussed. In its empirical application to two stock series for AstraZeneca and Ericsson B, we find that both series have a fractional integration property. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the intra-participant variabilities (i.e. intra-day and inter-day reliabilities) in leg and vertical stiffness, as well as related kinematic parameters, during treadmill running using the sine-wave method. Twenty-two healthy men ran on a treadmill at 4.44 m/s, and the flight and contact times were measured with a high-speed video camera. Three 30-s running bouts with 2-min inter-bout rests were performed to examine the intra-day reliability, and single 30-s running bouts on three separate days with 24- to 48-h inter-bout intervals were performed to examine the inter-day reliability. The reliability statistics included repeated-measure analyses of variance, average inter-trial correlations, intra-class correlation coefficients (ICCs), Cronbach's α reliability coefficient, and the coefficient of variation. Both leg and vertical stiffness produced high ICCs within 0.972 and 0.982, respectively, and between 0.922 and 0.873 days, respectively. High values were also observed for all of the reliability coefficients. Similar results were found regarding contact time, flight time, step length, and step rate. It was concluded that the measurements of leg and vertical stiffness, as well as related kinematic parameters, obtained using the sine-wave method during treadmill running at 4.44 m/s, were highly reliable, both within and across days. [ABSTRACT FROM PUBLISHER]

Background: A growing number of studies have reported an increased risk of cardiovascular disease (CVD) and respiratory disease (RD) within hours after exposure to ambient air pollution or temperature. We assemble published evidence on the sub-daily associations of CVD and RD with ambient air pollution and temperature., Methods: Databases of PubMed and Web of Science were searched for original case-crossover and time-series designs of English articles examining the intra-day effects of ambient air pollution [particulate matter with aerodynamic diameter ≤2.5 μm (PM 2.5 ), ≤10 μm (PM 10 ), 2.5-10μm (PM 10-2.5 ), and < 7 μm (SPM), O 3 , SO 2 , NO 2 , CO, and NO] and temperatures (heat and cold) on cardiorespiratory diseases within 24 h after exposure in the general population by comparing with exposure at different exposure levels or periods. Meta-analyses were conducted to pool excess risks (ERs, absolute percentage increase in risk) of CVD and RD morbidities associated with an increase of 10 μg/m 3 in particulate matters, 0.1 ppm in CO, and 10 ppb in other gaseous pollutants., Findings: Final analysis included thirty-three papers from North America, Europe, Oceania, and Asia. Meta-analysis found an increased risk of total CVD morbidity within 3 h after exposure to PM 2.5 [ER%: 2.65% (95% CI: 1.00% to 4.34%)], PM 10-2.5 [0.31% (0.02% to 0.59%)], O 3 [1.42% (0.14% to 2.73%)], and CO [0.41% (0.01% to 0.81%)]. The risk of total RD morbidity elevated at lag 7-12 h after exposure to PM 2.5 [0.69% (0.14% to 1.24%)] and PM 10 [0.38% (0.02% to 0.73%)] and at lag 12-24 h after exposure to SO 2 [2.68% (0.94% to 4.44%)]. Cause-specific CVD analysis observed an increased risk of myocardial infarction morbidity within 6 h after exposure to PM 2.5 , PM 10 , and NO 2 , and an increased risk of out-of-hospital cardiac arrest morbidity within 12 h after exposure to CO. Risk of total CVD also increased within 24 h after exposure to heat., Interpretation: This study supports a sudden risk increase of cardiorespiratory diseases within a few hours after exposure to air pollution or heat, and some acute and highly lethal diseases such as myocardial infarction and cardiac arrest could be affected within a shorter time., Funding: The National Natural Science Foundation of China (Grant No. 42105165; 81773518), the High-level Scientific Research Foundation of Anhui Medical University (Grant No. 0305044201), and the Discipline Construction of Anhui Medical University (Grant No. 0301001836)., Competing Interests: Declaration of interests All the authors declared no conflict of interests., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Much of the current research relating to forecasting the behavior of financial markets only consider returns in their models and forgets that adage 'it is volume that moves the market'. In this paper, we analyse the importance of volume, as a proxy of the information arrival, when forecasting the intraday volatility of the IBEX 35 index future. In forecasting intraday volatility, we use a selection of models, including symmetric, asymmetric and long-memory component structures in their response to information arrival. Results suggest that at high frequencies the inclusion of a proxy for information flow improves forecasting performance, while this is less true at the daily horizon. Nonetheless, asymmetric models are supported at all frequencies. [ABSTRACT FROM AUTHOR]

A bivariate integer-valued moving average (BINMA) model is proposed. The BINMA model allows for both positive and nagative correlation between the counts. This model can be seen as an inverse of the conditional duration model in the sense that short durations in a time interval correspond to a large count and vice versa. The conditional mean, variance, and covariance of the BINMA model are given. Model extensions to include explanatory variables are suggested. Using the BINMA model for AstraZeneca and Ericsson B , it is found that there is positive correlation between the stock transactions series. Empirically, we find support for the use of long-lag bivariate moving average models for the two series. [ABSTRACT FROM AUTHOR]

Recent research has suggested that intra-day volatility may possess a component structure, resulting either from the arrival of heterogeneous information or the actions of heterogeneous market agents. This paper reports direct evidence for the existence of such components in S&P500 index and DM/$ exchange rate data. Estimation of a FIGARCH model supports the contention that volatility dynamics result from multiple sources. Using a HARCH conditional variance model which defines volatility components over differing time horizons, confirmatory evidence of heterogeneous components is reported, in which context the impact of high-frequency speculation and noise-trading are particularly apparent. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• Instantaneous direct, diffuse, and total FAPARs estimated from the Himawari-8 AHI. • The total and direct FAPARs show a "bowl-shaped" intra-daily variation pattern. • The remote sensed total and direct FAPARs at 10:00–10:30 LST underestimate the daily FAPARs. • The total and direct FAPARs at 9:30 and 14:30 LST best approximate the daily values. The fraction of absorbed photosynthetically active radiation (FAPAR) quantifies the efficiency of light absorption by vegetation. The intra-daily variation in FAPAR is important for monitoring vegetation growth but current studies to estimate the intra-daily FAPAR from remote sensing are limited. This study proposes to estimate the intra-daily variations of the direct, diffuse, and total FAPARs on clear days from the geostationary Himawari-8 AHI data. A look-up table inversion method was developed based on simulations of a radiative transfer model that couples a soil reflectance (GSV, General Spectral Vectors), canopy (PROSAIL), and atmospheric models (6SV, Second Simulation of a Satellite Signal in the Solar Spectrum). Field measurements were conducted over paddy rice fields in Northeast China from June 22 to August 25, 2019, to obtain continuous FAPAR measurements using an AccuPAR ceptometer and digital hemispherical photography. The instantaneous FAPAR values were analyzed and evaluated with field measurements and MODIS and GEOV2 FAPAR products. The results show that the retrieved AHI FAPAR values are consistent with the field measurements (R2 > 0.75). The AHI, MODIS, and GEOV2 FAPAR values display similar spatial and temporal patterns over the growing season (R2 > 0.66). The intra-daily variations in the total and direct FAPAR values agree with the field measurements, and both show a "bowl-shaped" pattern with the smallest value found at noon. The diffuse FAPAR remains stable before 16:00 local solar time (LST). The instantaneous total and direct FAPAR values at 10:00 – 10:30 LST usually assumed to be daily values underestimate the daily values, while the values at 9:30 and 14:30 LST can better represent the daily FAPAR. As a future study, global intra-daily instantaneous and daily FAPAR products could be generated by combining all geostationary satellite data. [ABSTRACT FROM AUTHOR]

• A redispatch scheme for heterogenous resources with accurate grid and battery models. • Scenario-based optimization to account for stochastic injections. • Handles a large scenarios number and high computational complexity in a tractable way. • Intra-day re-dispatch eliminates tracking error for practical battery capacities. • Operational costs due to reserve activation reduced by a factor 3. In this paper, we propose a re-dispatch scheme for radial distribution grids hosting stochastic Distributed Energy Resources (DERs) and controllable batteries. At each re-dispatch round, the proposed scheme computes a new dispatch plan that modifies and extends the existing one. To do so, it uses the CoDistFlow algorithm and applies a receding horizon control principle, while accounting for hard time computation constraints that impact on the instantaneous update of a dispatch plan. CoDistFlow handles stochastic DERs and prosumers uncertainties via scenario-based optimization and the non-convexity of the AC Optimal Power Flow by iteratively solving suitably defined convex problems until convergence. We perform numerical evaluations based on real-data, obtained from a real Swiss grid. We show that, with our proposed re-dispatch scheme, the daily dispatch tracking error can decrease more than 80%, even for small battery capacities, and if re-dispatch is frequent enough, it can be eliminated. Finally, we show that re-dispatch should be performed as often as the market allows and the performance continues to improve. [ABSTRACT FROM AUTHOR]

In order to better handle the new features that emerge at both ends of supply and demand, new measures are constantly being introduced, such as demand-side management (DSM) and prediction of uncertain output and load. However, the existing DSM strategies, like real-time price (RTP), and dispatch methods are optimized separately, and response models of active loads, such as the interruptible load (IL), are still imperfect, which make it difficult for the active distribution network (ADN) to achieve global optimal operation. Therefore, to better manage active loads, the response characteristics including both the response time and the responsibility and compensation model of IL for cluster users, and the real-time demand response model for price based load, were analyzed and established. Then, a collaborative optimization strategy of RTP and optimal dispatch of ADN was proposed, which can realize an economical operation based on mutual benefit and win-win mode of supply and demand sides. Finally, the day-ahead and intra-day integrative dispatch model using different time-scale prediction data was established, which can achieve longer-term optimization while reducing the impact of prediction errors on the dispatch results. With numerical simulations, the effectiveness and superiority of the proposed strategy were verified. [ABSTRACT FROM AUTHOR]