Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

Frequency Control Approach and Load Forecasting Assessment for Wind Systems

Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature.Whenever there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy systems.In this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems.Frequency control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning models.The performance analysis of the proposed method is done with different machine learning based approaches.The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy.Simulation results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.

SELF-LEARNING FUZZY CONTROL RULES USING GENETIC ALGORITHMS

This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.

Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm

This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.

Neuron self-learning PSD control for backside width of weld pool in pulsed GTAW with wire filler

In this paper, the weld pool shape control by intelligent strategy was studied. A neuron self-learning PSD controller for backside width of weld pool in pulsed GTAW with wire filler was designed. The PSD control arithmetic was analyzed, simulating experiment by MATLAB software was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were successfully implemented. The study results show that the neuron self-learning PSD control method can attain a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.

Self-Learning and Its Application to Laminar Cooling Model of Hot Rolled Strip

The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.

Microseismic monitoring and forecasting of dynamic disasters in underground hydropower projects in southwest China:A review

The underground hydropower projects in Southwest China is characterized by large excavation sizes,high geostresses,complicated geological conditions and multiple construction processes.Various disasters such as collapses,large deformations,rockbursts are frequently encountered,resulting in serious casualties and huge economic losses.This review mainly presents some representative results on microseismic(MS)monitoring and forecasting for disasters in hydropower underground engineering.First,a set of new denoising,spectral analysis,and location methods were developed for better identification and location of MS signals.Then,the tempo-spatial characteristics of MS events were analyzed to understand the relationship between field construction and damages of surrounding rocks.Combined with field construction,geological data,numerical simulation and parametric analysis of MS sources,the focal mechanism of MS events was revealed.A damage constitutive model considering MS fracturing size was put forward and feedback analysis considering the MS damage of underground surrounding rocks was conducted.Next,an MS multi-parameter based risk assessment and early warning method for dynamic disasters were proposed.The technology for control of the damage and deformation of underground surrounding rocks was proposed for underground caverns.Finally,two typical underground powerhouses were selected as case studies.These achievements can provide significant references for prevention and control of dynamic disasters for underground engineering with similar complicated geological conditions.

ADAPTIVE FORECAST AND CONTROL OF THE MARKET ECONOMIC SYSTEM WITH FUZZY INPUTS

In this paper, the adaptive forecast and control of the market economic system with fuzzy inputs is discussed. A new method which is adapted for the adaptive forecast and control of this kind of system is introduced. Through a living example the better result is explained concretly.

A Computer System for Forecasting the Threshold Period for Crop Weed Control

In this article, a model of a weed control threshold forecast system has been established, with related model solving, data checking, database setting up, and system engineering illustration. Moreover, it is tested by a software with data from a sugar cane planting experimental field in Yunnan, China. The methodology behind the detailed system analysis, design, and engineering has been discussed. The issue of how to create a dynamic data-dependent forecast model of a threshold forecast system, whose threshold changes according to the change of planting environment has been solved. Hence an effective solution has been initiated for further development on an agricultural expert system.

A Clustering-tree Topology Control Based on the Energy Forecast for Heterogeneous Wireless Sensor Networks

How to design an energy-efficient algorithm to maximize the network lifetime in complicated scenarios is a critical problem for heterogeneous wireless sensor networks(HWSN).In this paper, a clustering-tree topology control algorithm based on the energy forecast(CTEF) is proposed for saving energy and ensuring network load balancing, while considering the link quality, packet loss rate, etc. In CTEF, the average energy of the network is accurately predicted per round(the lifetime of the network is denoted by rounds) in terms of the difference between the ideal and actual average residual energy using central limit theorem and normal distribution mechanism, simultaneously. On this basis, cluster heads are selected by cost function(including the energy, link quality and packet loss rate) and their distance.The non-cluster heads are determined to join the cluster through the energy, distance and link quality. Furthermore, several noncluster heads in each cluster are chosen as the relay nodes for transmitting data through multi-hop communication to decrease the load of each cluster-head and prolong the lifetime of the network. The simulation results show the efficiency of CTEF. Compared with low-energy adaptive clustering hierarchy(LEACH), energy dissipation forecast and clustering management(EDFCM) and efficient and dynamic clustering scheme(EDCS)protocols, CTEF has longer network lifetime and receives more data packets at base station.

合流制溢流污染的影响及其控制技术发展

合流制溢流即未经处理的雨污废水直接流入受纳水体,是造成受纳水体污染和城市内涝的主要来源。首先,对合流制溢流的污染特征及其影响因素进行了总结,简要梳理了源头控制、处理设施、调蓄设施、雨天合流雨污水处理最新技术及其控制效果,以及合流制溢流控制实时控制、监测和预测的智能控制方法。合流制溢流灰色和绿色基础设施都是必要的,随着监测、降水预报、城市水文和水力模型及实时控制技术的成熟,合流制溢流系统向实时控制和模型优化预测策略发展。

多源数据融合的深埋隧道岩爆预测方法

为提高隧道设计阶段未开挖区域的岩爆预测准确性,提出了一种多源数据融合的隧道岩爆预测方法.综合隧道勘察和施工阶段的不同地质信息,采用基于证据关联系数的加权融合技术,构建了隧道精细化三维动态地质模型.建立了基于强度理论的岩爆可能性判定方法和基于能量理论的岩爆烈度预测方法,通过Hoek-Brown强度准则判断围岩是否发生岩爆,利用储能极限阈值和能量释放指数划分岩爆烈度,并将其应用于四川某隧道工程中.结果表明,所提方法可以实现隧道掌子面前方30 m范围内的岩爆精准预测,预测结果与隧道开挖实际岩爆的吻合率为95.8%.因此,该预测方法能够在隧道施工前预判岩爆烈度,为隧道岩爆防治提供指导.

基于模态分解及GRU-XGBoost短期电力负荷预测

精确的短期电力负荷预测能有效提高电力系统运营水平。针对电力负荷数据受多种因素影响,波动性和随机性强等问题,提出了一种基于模态分解及混合模型的负荷预测方法。首先,采用主成分分析法(principal component analysis,PCA)对负荷特征向量进行处理,去掉冗余信息,再用完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)将历史负荷分解为简化的几个子序列;其次,选择引入样本熵(sample entropy,SE)来计算子序列熵值,将相近的子序列重构得到随机、细节、低频和趋势分量后选用不同结构门控循环单元(gate recurrent unit,GRU)对不同分量类型进行预测,再使用极致梯度提升模型(extreme gradient boosting,XGBoost)对各分量残差进行拟合,各重组序列的预测值为GRU预测值与XBGoost拟合值之和,重组各序列得到最终预测值。选取3年时电力负荷数据进行实验,结果表明,所提模型的均方根误差(root mean square error,RMSE)、平均绝对百分比误差(mean absolutepercentage error,MAPE)和平均绝对误差(mean absolute error,MAE)分别为370.676 MW、99.07%和246.89 MW,与单一模型和混合模型相比,实现了评价指标的明显减少。

改进Transformer在产油量预测中的应用研究

产油量预测有利于制定合理的采油策略。本文提出一种包含卷积神经网络、门控循环单元和Transformer的组合模型CNN-GRU-Transformer,可用于产油量预测。该模型应用CNN提取部分深层空间特征,GRU提取产油量数据的时序特征,并根据油井数据的特点,改进了Transformer原有结构。通过改进的Transformer,将提取到的特征与预测相结合。实验的结果表明,CNN-GRU-Transformer模型在预测产油量各项指标中均为最优值,在适应产油量基本趋势方面表现最佳。

芦岭煤矿10号煤层瓦斯赋存及其控制因素综合评价

为准确、快速预测矿井煤层瓦斯赋存情况并确定其控制因素,利用分源预测法和模糊物元评价方法,分析了芦岭煤矿10号煤层瓦斯赋存规律,并开展了瓦斯赋存控制因素综合评价。研究结果表明:10号煤层瓦斯涌出量总体较高,具有“中、西部高,东南部低”的分布特点,且瓦斯涌出量随着深度的增加而增大,其涌出量范围在9.23~16.11 m^(3)/t;研究区根据控制因素评价划分为3类区域,其中西南浅部为Ⅰ类区域,东北深部多为Ⅱ类、Ⅲ类区域。

富满地区断控缝洞型油藏试油初期产能预测方法

塔里木盆地富满地区碳酸盐岩缝洞型油藏产能预测分析多是定性认识,且产能预测方程可操作性较差。通过分析稳定渗流压裂井产量与井底压力关系,发现影响各井产能的主要因素是生产压差和裂缝发育程度。根据钻井期间目的层钻具放空和泥浆漏失量情况将富满地区试油井分为3类,同时分别统计分析3类井生产压差和日产油、日产气的关系,最终形成了1套符合富满地区断控缝洞型油藏的产能预测方法。利用该方法对富满地区7口试油井进行产量预测,有5口井试油后验证产量误差在15%以内,符合率达到71.43%。该方法使用简单、可靠性强,可在塔里木盆地富满地区推广应用。

新疆管制区域扇区容量评估与划设研究

新疆是我国与中亚、欧洲连接的重要陆路通道,是我国“一带一路”陆上至关重要的支点。随着航班量的逐年递增,新疆管制区域的管制压力逐年增大,负荷分布不均匀、扇区内冲突点增多的问题愈发严重。面向我国“一带一路”和“空中丝绸之路”的发展要求,预测2035年新疆管制区域的流量增长情况;基于管制员的工作负荷对当前的扇区划分方案进行容量评估,结合流容比指标对新疆区域2035年的扇区划分提出合理化的建议以满足流量增长的保障需求;采用计算机仿真技术对2035年扇区划设方案进行仿真评估。结果表明:新的扇区划分能够有效地减少高峰日流量和高峰小时流量,之前管制员负荷较高的扇区通过合理地划分,管制压力也得到了明显缓解。

GIS技术在洪水灾害中的应用研究综述

地理信息系统(GIS)是一种专用于处理地理和空间信息的计算机系统。当前,随着计算机技术的逐渐成熟,GIS技术的空间分析等特点对于水资源的规划与管理有着重要的研究意义,其中在洪水灾害中的应用是近年来研究发展的一个重点。通过GIS技术建立洪水模拟模型,进行预测预警,以此为防洪抗涝提出更加科学的决策建议,降低频发的洪涝灾害事件所带来的损失。该文针对其在洪水灾害中的预测、风险图绘制、模型建立及避险路线进行评述,提出当前存在的问题,并对今后GIS在洪水灾害中的应用进行展望。