Nowadays the researches of building energy efficiency mainly focus on the development of the related technologies and management measures, but the regional policy EIA have a greater impact on the development of buildi...
详细信息
ISBN:
(纸本)9783037854853
Nowadays the researches of building energy efficiency mainly focus on the development of the related technologies and management measures, but the regional policy EIA have a greater impact on the development of building energy efficiency in China. In this paper, the statistical data combined with the actual situation of building energy efficiency during the period of the Eleventh-Five-Year in Anhui Province, China. Use exponential smoothing method of time series algorithm for forecast and analysis of building energy consumption and energy saving in Anhui Province. First, take six different values as the exponential smoothing constants, the predictive values are calculated under the different exponential smoothing constants. Then compare and analyze the curves through the errors between the predictive values and the actual values, and determine the group of the curves of which the trend closest to the trend of actual values. For the actual data are affected by many kinds of factors that exist fluctuations, the method for predictive values correction was put forward. Using the trend extrapolation, the predictive values is corrected. Also provides theoretical guidance for the future work in Anhui Province and other similar places.
In this paper, we propose a vendor-to-retailers vegetable and fruit ordering application that streamlines the supply chain process. The application provides a platform for vendors and retailers to interact and conduct...
详细信息
This study investigates the relationship between sunspot activity and solar cycle variations, utilizing time series algorithms and the CNN-LSTM model for prediction. Initial preprocessing of sunspot number and area da...
详细信息
The stability of main steam temperature in thermal power plants is especially important for boiler operation. Conventional PID has poor control effect on large delay targets of main steam temperature system in thermal...
详细信息
ISBN:
(纸本)9781728113128
The stability of main steam temperature in thermal power plants is especially important for boiler operation. Conventional PID has poor control effect on large delay targets of main steam temperature system in thermal power plants. It is difficult to achieve satisfactory control results. In view of this situation, a PID-based fuzzy controller based on the timeseries prediction algorithm was designed The time series algorithm can predict the main steam temperature at the next moment and calculate the input value of the PID fuzzy controller according to the predicted value. The fuzzy control that has the leading characteristic and the regulator advances to reduce the overshoot and adjustment time obviously. The BP neural network algorithm is used to correct the prediction results of the time series algorithm, which makes the algorithm more stable and safe The simulation and experimental results show that the control effect is significantly better, indicating that this is an effective improvement method, which is obviously superior to the traditional PID control and PID-type fuzzy control.
Purpose - This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social **...
详细信息
Purpose - This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social ***/methodology/approach - In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied timeseries models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public *** - The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable ***/value - The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.
Weather parameters and weather forecasting play a crucial role in scientific experiments for the safe operation of the system. Weather prediction involves high-performance computing, which enables building of weather ...
详细信息
ISBN:
(纸本)9798350383782;9798350383799
Weather parameters and weather forecasting play a crucial role in scientific experiments for the safe operation of the system. Weather prediction involves high-performance computing, which enables building of weather models. Major Atmospheric Cherenkov Experiment (MACE) is a 21m ground-based, very high-energy gamma-ray telescope installed at Hanle, India. Accurate and reliable short-term weather prediction plays a crucial role in ensuring the safe and efficient functioning of the telescope. In the first part of the paper, we describe the multithreaded design of the real-time weather data capture application for the telescope. In the second part, we compare the performance of time series algorithms for weather prediction and the possibility of hybrid models for predicting non-stationary weather factors.
The present photovoltaic (PV) power generation systems are globally facing the irregularity problem in the distribution of PV generation. In particular, the exact PV power forecasting is critical for grid-connected ph...
详细信息
The present photovoltaic (PV) power generation systems are globally facing the irregularity problem in the distribution of PV generation. In particular, the exact PV power forecasting is critical for grid-connected photovoltaic (PV) systems under unwanted changes in environmental circumstances. The grid energy manage-ment, grid operation and scheduling are important factors to forecast the PV power output. timeseries analysis is one of the most important aspects of PV output prediction, especially in places (in South Korea) where past solar radiation data or other weather parameters have not been recorded. In this paper, a variety of time-series methods including deep-learning algorithm and machine learning algorithms was used to predict the PV power generation output for quick respond to equipment and panel defects. For designing AI models, the input data were characterized by dividing seasons and choosing the multiple parameters from seasons. In this study, the photovoltaic power generation data was collected from Ansan city, South Korea during January 2017 to June 2021 and the weather data was collected from Suwon city, South Korea during January 2017 to June 2021. In this work, approx. 40,000 hours of operation data from 1.5 MW grid-connected PV system in South Korea was used. PV power generation forecasting was carried out on an hourly basis to test efficacy of various models. Among all models (Holt-Winters, Multivariate Linear Regression, ARIMA, SARIMA, ARIMAX, SARIMAX), LSTM model presented the lowest error rate as compared to other models for quick PV power generation forecasting.
The fluctuation of load of power systems is influenced by many factors. Mid-long term load forecasting is not only based on the information and materials of the power system itself, but is also limited by those of the...
详细信息
The fluctuation of load of power systems is influenced by many factors. Mid-long term load forecasting is not only based on the information and materials of the power system itself, but is also limited by those of the social and economic as well as other factors of the area. Taking into consideration the influences of non-power systems, this paper has classified the different types of loads and proposed the basic conception of a 'load norm' of power systems and the physical seriesalgorithm, and finally set up the mathematical models for the relevant mid-long term load forecasting. This paper has used this method to forecast the total consumption of the power load for a province in East China and that of the heavy industry of another province in Northwest China. The results proved to have a high precision of inverse calculation and forecasting credibility. (C) 2000 Elsevier Science S.A. All rights reserved.
This study aims to explore the timeseries context and sentiment polarity features of rumors' life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The propos...
详细信息
This study aims to explore the timeseries context and sentiment polarity features of rumors' life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The proposed model is a convolutional neural network embedded with an attention mechanism of sentiment polarity and timeseries information. Firstly, the whole life cycle of rumors is divided into 20 groups by the time series algorithm and each group of texts is trained by Doc2Vec to obtain the text vector. Secondly, the SVM algorithm is used to obtain the sentiment polarity features of each group. Lastly, the CNN model with the spatial attention mechanism is used to obtain the rumors' classification. The experiment results show that the proposed model introduced with features of timeseries and sentiment polarity is very effective for rumor detection, and can greatly reduce the number of iterations for model training as well. The accuracy, precision, recall and F1 of the attention CNN are better than the latest benchmark model.
Large enterprises essentially require a reliable infrastructure that can store, retrieve and analyze the massive volumes of high dimensional data. Knowledge Discovery in Databases (KDD) through Data Mining (OM) presen...
详细信息
ISBN:
(纸本)9781479933518
Large enterprises essentially require a reliable infrastructure that can store, retrieve and analyze the massive volumes of high dimensional data. Knowledge Discovery in Databases (KDD) through Data Mining (OM) presents a powerful tool for storing and retrieving data in a manner that optimizes performance as well as resources. This work presents the application of timeseries Data Mining algorithm to Hospital Management Information System (HMIS) in a public sector hospital in Pakistan. Public sector hospitals in Pakistan, apart from being typically overcrowded are severely limited by lack of resources. This research focuses on the cost effective application of KDD alongside correctly predicting disease patterns, hospital admittance rate and patient turn out. The results of this work not only bring about an improvement in management for this hospital but also provide a model for other health care facilities in the developing world.
暂无评论