An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with Predictive Machine Learning Approach

Virtual Machines are the core of cloud computing and are utilized toget the benefits of cloud computing. Other essential features include portability,recovery after failure, and, most importantly, creating the core mechanismfor load balancing. Several study results have been reported in enhancing loadbalancingsystems employing stochastic or biogenetic optimization methods.It examines the underlying issues with load balancing and the limitationsof present load balance genetic optimization approaches. They are criticizedfor using higher-order probability distributions, more complicated solutionsearch spaces, and adding factors to improve decision-making skills. Thus, thispaper explores the possibility of summarizing load characteristics. Second,this study offers an improved prediction technique for pheromone level predictionover other typical genetic optimization methods during load balancing.It also uses web-based third-party cloud service providers to test and validatethe principles provided in this study. It also reduces VM migrations, timecomplexity, and service level agreements compared to other parallel standardapproaches.

Estimation of gases emitted by forest fires based on remote sensing data

Forest fire, an important agent for change in many forest ecosystems, plays an important role in atmo- spheric chemical cycles and the carbon cycle. The primary emissions from forest fire, CO2, CO, CH4, long-chained hydrocarbons and volatile organic oxides, however, have not been well quantified. Quantifying the carbonaceous gas emissions of forest fires is a critical part to better under- stand the significance of forest fire in calculating carbon balance and forecasting climate change. This study uses images from Enhanced Thematic Mapper Plus （ETM＋） on the Earth-observing satellite LANDSAT-7 for the year 2005 to estimate the total gases emitted by the 2006 Kanduhe forest fire in the Daxing＇an Mountains. Our results suggest that the fire emitted approximately 149,187.66 t CO2, 21,187.70 t CO, 1925.41 t CxHy, 470.76 t NO and 658.77 t SO2. In addition, the gases emitted from larch forests were significantly higher than from both broadleaf-needle leaf mixed forests and broadleaf mixed forests.

Peak Load Evaluation Based on the Accumulated Annual Energy

This paper presents a nonlinear approach to estimate the consumed energy in electric power distribution feeders. The proposed method uses the statistical solution algorithm to analyze the active energy monthly consumption, which enables one to estimate the energy consumption during any period of the year. The energy readings and the normalized accumulated energy profile are used to estimate the hourly consumed active power, which can be used for future planning and sizing the equipment of the electrical system. The effectiveness of the proposed method is demonstrated by comparing the simulated results with that of real measured data.

面向城镇住宅建筑光伏一体化的GIS开放数据协同仿真平台

The rising awareness of environmental issues and the increase of renewable energy sources(RESs)has led to a shift in energy production toward RES,such as photovoltaic(PV)systems,and toward a distributed generation(DG)model of energy production that requires systems in which energy is generated,stored,and consumed locally.In this work,we present a methodology that integrates geographic information system(GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles.In particular,we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’electricity demand.Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation.It integrates methodologies to estimate energy demand with a high temporal resolution,accounting for realistic populations with realistic consumption profiles.Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities.The proposed methodology is tested and validated within the municipality of Turin,Italy.For the whole municipality,we estimate both the electricity absorbed from the residential sector(simulating a realistic population)and the electrical energy that could be produced by installing PV systems on buildings’rooftops(considering two different scenarios,with the former using only the rooftops of residential buildings and the latter using all available rooftops).The capabilities of the platform are explored through an in-depth analysis of the obtained results.Generated power and energy profiles are presented,emphasizing the flexibility of the resolution of the spatial and temporal results.Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO_(2) emissions.

Water quality prediction based on sparse dataset using enhanced machine learning

Water quality in surface bodies remains a pressing issue worldwide.While some regions have rich water quality data,less attention is given to areas that lack sufficient data.Therefore,it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly.Here we showed sparse-dataset-based prediction of water pollution using machine learning.We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory(LSTM)models,integrated with the Load Estimator(LOADEST).The research was conducted at a river-lake confluence,an area with intricate hydrological patterns.We found that the Self-Attentive LSTM(SA-LSTM)model outperformed the other three machine learning models in predicting water quality,achieving Nash-Sutcliffe Efficiency(NSE)scores of 0.71 for COD_(Mn)and 0.57 for NH_(3)N when utilizing LOADEST-augmented water quality data(referred to as the SA-LSTMLOADEST model).The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error(RMSE)by 24.6%for COD_(Mn)and 21.3%for NH_(3)N.Furthermore,the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly.Additionally,the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance.This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.

Location estimating of load for bending beam

We propose a method by which location of load for bending beam can be approxi- mately retrieved by matching the measured and theoretically forecasted displacement distribu- tion. To show the method validity, a principal experiment is performed.

Occurrence and risk assessment of typical PPCPs and biodegradation pathway of ribavirin in wastewater treatment plants

A large number of pharmaceuticals and personal care products(PPCPs)persist in wastewater,and the consumption of PPCPs for COVID-19 control and prevention has sharply increased during the pandemic.This study investigated the occurrence,removal efficiency,and risk assessment of six typical PPCPs commonly used in China in two wastewater treatment plants(WWTPs).Ribavirin(RBV)is an effective pharmaceutical for severely ill patients with COVID-19,and the possible biodegradation pathway of RBV by activated sludge was discovered.The experimental results showed that PPCPs were detected in two WWTPs with a detection rate of 100%and concentrations ranging between 612 and 2323 ng L^(-1).The detection frequency and concentrations of RBV were substantially higher,with a maximum concentration of 314 ng L^(-1).Relatively high pollution loads were found for the following PPCPs from influent:ibuprofen>ranitidine hydrochloride>RBV>ampicillin sodium>clozapine>sulfamethoxazole.The removal efficiency of PPCPs was closely related to adsorption and biodegradation in activated sludge,and the moving bed biofilm reactor(MBBR)had a higher removal capacity than the anoxic-anaerobic-anoxicoxic(AAAO)process.The removal efficiencies of sulfamethoxazole,ampicillin sodium,ibuprofen,and clozapine ranged from 92.21%to 97.86%in MBBR process and were relatively low,from 61.82%to 97.62%in AAAO process,and the removal of RBV and ranitidine hydrochloride were lower than 42.96%in both MBBR and AAAO processes.The discrepancy in removal efficiency is caused by temperature,hydrophilicity,and hydrophobicity of the compound,and acidity and alkalinity.The transformation products of RBV in activated sludge were detected and identified,and the biodegradation process of RBV could be speculated as follows:first breaks into TCONH_(2) and an oxygen-containing five-membered heterocyclic ring under the nucleosidase reaction,and then TCONH_(2) is finally formed into TCOOH through amide hydrolysis.Aquatic ecological risks based on risk quotient(RQ)assessment showed that PPCPs had high and medium risks in the influent,and the RQ values were all reduced after MBBR and AAAO treatment.Ranitidine hydrochloride and clozapine still showed high and medium risks in the effluent,respectively,and thus presented potential risks to the aquatic ecosystem.