Load Profile Analysis for Mitigating Load-Shedding in Central Africa: Case of Kinshasa

Load shedding is a major problem in Central Africa, with negative consequences for both society and the economy. However, load profile analysis can help to alleviate this problem by providing valuable information about consumer demand. This information can be used by power utilities to forecast and reduce power cuts effectively. In this study, the direct method was used to create load profiles for residential feeders in Kinshasa. The results showed that load shedding on weekends results in significant financial losses and changes in people’s behavior. In November 2022 alone, load shedding was responsible for $ 23,4 08,984 and $ 2 80,9 07,808 for all year in losses. The study also found that the SAIDI index for the southern direction of the Kinshasa distribution network was 122.49 hours per feeder, on average. This means that each feeder experienced an average of 5 days of load shedding in November 2022. The SAIFI index was 20 interruptions per feeder, on average, and the CAIDI index was 6 hours, on average, before power was restored. This study also proposes ten strategies for the reduction of load shedding in the Kinshasa and central Africa power distribution network and for the improvement of its reliability, namely: Improved load forecasting, Improvement of the grid infrastructure, Scheduling of load shedding, Demand management programs, Energy efficiency initiatives, Distributed Generation, Automation and Monitoring of the Grid, Education and engagement of the consumer, Policy and regulatory assistance, and Updated load profile analysis.

Review of Load Balancing Mechanisms in SDN-Based Data Centers

With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.

Load Balancing-Based Routing Optimization Mechanism for Power Communication Networks

In power communication networks, it is a challenge to decrease the risk of different services efficiently to improve operation reliability. One of the important factor in reflecting communication risk is service route distribution. However, existing routing algorithms do not take into account the degree of importance of services, thereby leading to load unbalancing and increasing the risks of services and networks. A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems. First, the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices, service load, and service characteristics. Second, service weights are determined with modified relative entropy TOPSIS method, and a balanced service routing determination algorithm is proposed. Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.

A Coverage-Aware Unequal Clustering Protocol with Load Separation for Ambient Assisted Living Based on Wireless Sensor Networks

Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.

A Novel Load Balancing Strategy of Software-Defined Cloud/Fog Networking in the Internet of Vehicles

The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.

Load-redistribution strategy based on time-varying load against cascading failure of complex network

Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently.

Prediction of Rolling Load in Hot Strip Mill byInnovations Feedback Neural Networks

Because the structure of the classical mathematical model of rolling load is simple, even with the self-adapting technology, it is difficult to accommodate the increasing dimensional accuracy. Motivated by this fact, an Innovations Feedback Neural Networks （IFNN） was presented based on the idea of Kalman prediction. The neural networks used the Back Propagation （BP） algorithm and applied it to the prediction of rolling load in hot strip mill. The theoretical results and the off-line simulation show that the prediction capability of IFNN is better than that of normal BP networks, namely, for the prediction of the rolling load in hot strip mill, the prediction precision of IFNN is higher than that of normal BP networks. Finally, a relative complete rolling load prediction system was developed on Windows 2003/XP platform using the OOP programming method and the SQL server2000 database. With this sys- tem, the rolling load of a 1700 strip mill was calculated, and the prediction results obtained correspond well with the field data. It shows that IFNN is valid for rolling load prediction.

Dynamic Multi-class Network Loading Problem

The dynamic network loading problem (DNLP) consists in determining on a congested network, time-dependent arc volumes, together with arc and path travel times, given the time varying path flow departure rates over a finite time horizon. The objective of this pap er is to present the formulation of an analytical dynamic multi-class network loading model. The mo del does not require the assumption of the FIFO condition. The existence of a solution to the model is shown.

Spatial Load Balancing in Wide-Area Wireless Networks

Load balancing is typically used in the frequency domain of cellular wireless networks to balance paging, access, and traffic load across the available bandwidth. In this paper, we extend load balancing into the spatial domain, and we develop two approaches--network load balancing and single-carrier multilink--for spatial load balancing. Although these techniques are mostly applied to cellular wireless networks and Wi-Fi networks, we show how they can be applied to EV-DO, a 3G cellular data network. When a device has more than one candidate server, these techniques can be used to determine the quality of the channel between a server and the device and to determine the Ipad on each server. The proposed techniques leverage the advantages of existing EV-DO network architecture and are fully backward compatible. Network operators can substantially increase network capacity and improve user experience by using these techniques. Combining load balancing in the frequency and spatial domains improves connectivity within a network and allows resources to be optimally allocated according to the p-fair criterion. Combined load balancing further improves performance.

The Development of Highly Loaded Turbine Rotating Blades by Using 3D Optimization Design Method of Turbomachinery Blades Based on Artificial Neural Network & Genetic Algorithm

In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.

Load Balancing Fat-Tree on Long-Lived Flows:Avoiding Congestion in a Data Center Network

In a data center network （DCN）, load balancing is required when servers transfer data on the same path. This is necessary to avoid congestion. Load balancing is challenged by the dynamic transferral of demands and complex routing control. Because of the distributed nature of a traditional network, previous research on load balancing has mostly focused on improving the performance of the local network; thus, the load has not been optimally balanced across the entire network. In this paper, we propose a novel dynamic load-balancing algorithm for fat-tree. This algorithm avoids congestions to the great possible extent by searching for non-conflicting paths in a centralized way. We implement the algorithm in the popular software-defined networking architecture and evaluate the algorithm＇ s performance on the Mininet platform. The results show that our algorithm has higher bisection band- width than the traditional equal-cost multi-path load-balancing algorithm and thus more effectively avoids congestion.

Load-Aware Offloading Strategy in Two-Tier Heterogeneous Network

Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations.Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.

Value analysis of district heating system with gas-fired peak load boiler in secondary network

In district heating(DH) system with gas-fired peak load regulating boiler in the secondary network,by prolonging run time of base load plants under rated condition,the mean energy efficiency could be increased. The fuels of the system,including coal and gas,would cause different environmental impacts. Meanwhile,the reliability of the heating networks would be changed because the peak load regulating boiler could work as a standby heat source. A model for assessment of heating system was established by value analysis to optimize this kind of system. Energy consumption,greenhouse gas emission,pollution emission and system reliability were selected as functional assessment indexes in the model. Weights of each function were determined by analytical hierarchy process (AHP) and experts consultation. Life cycle cost was used as the cost in the model. A real case as an example was discussed to obtain the optimal base load ratio. The result shows that the optimal base load ratio of the case is 0.77.

Using Artificial Neural Network to Estimate Sediment Load in Ungauged Catchments of the Tonle Sap River Basin, Cambodia

Concern on alteration of sediment natural flow caused by developments of water resources system, has been addressed in many river basins around the world especially in developing and remote regions where sediment data are poorly gauged or ungauged. Since suspended sediment load (SSL) is predominant, the objectives of this research are to: 1) simulate monthly average SSL (SSLm) of four catchments using artificial neural network (ANN);2) assess the application of the calibrated ANN (Cal-ANN) models in three ungauged catchment representatives (UCR) before using them to predict SSLm of three actual ungauged catchments (AUC) in the Tonle Sap River Basin;and 3) estimate annual SSL (SSLA) of each AUC for the case of with and without dam-reservoirs. The model performance for total load (SSLT) prediction was also investigated because it is important for dam-reservoir management. For model simulation, ANN yielded very satisfactory results with determination coefficient (R2) ranging from 0.81 to 0.94 in calibration stage and 0.63 to 0.87 in validation stage. The Cal-ANN models also performed well in UCRs with R2 ranging from 0.59 to 0.64. From the result of this study, one can estimate SSLm and SSLT of ungauged catchments with an accuracy of 0.61 in term of R2 and 34.06% in term of absolute percentage bias, respectively. SSLA of the AUCs was found between 159,281 and 723,580 t/year. In combination with Brune’s method, the impact of dam-reservoirs could reduce SSLA between 47% and 68%. This result is key information for sustainable development of such infrastructures.

Load Balancing in IP/MPLS Networks: A Survey

The present era has witnessed tremendous growth of the Internet and various applications that are supported by it. There is an enormous pressure on Internet Service Providers (ISPs) to make available adequate services for the traffics like VoIP and Video on demand. Since the resources like computing power, bandwidth etc. are limited, the traffic needs to be engineered to properly exploit them. Due to these limitations, terms like Traffic Engineering, Quality of Service (QoS) came into existence. Traffic Engineering broadly includes techniques like multipath routing & traffic splitting to balance the load among different paths. In this document, we survey various techniques proposed for load balancing that are available on the Internet. We here try not to be exhaustive but analyze the important techniques in the literature. Present survey would help to give a new direction to the research in this realm.

Load Reduction Test Method of Similarity Theory and BP Neural Networks of Large Cranes

Static load tests are an important means of supervising and detecting a crane＇s lift capacity. Due to space restrictions, however, there are difficulties and potential danger when testing large bridge cranes. To solve the loading problems of large-tonnage cranes during testing, an equivalency test is proposed based on the similarity theory and BP neural networks. The maximum stress and displacement of a large bridge crane is tested in small loads, combined with the training neural network of a similar structure crane through stress and displacement data which is collected by a physics simulation progressively loaded to a static load test load within the material scope of work. The maximum stress and displacement of a crane under a static load test load can be predicted through the relationship of stress, displacement, and load. By measuring the stress and displacement of small tonnage weights, the stress and displacement of large loads can be predicted, such as the maximum load capacity, which is 1.25 times the rated capacity. Experimental study shows that the load reduction test method can reflect the lift capacity of large bridge cranes. The load shedding predictive analysis for Sanxia 1200 t bridge crane test data indicates that when the load is 1.25 times the rated lifting capacity, the predicted displacement and actual displacement error is zero. The method solves the problem that lifting capacities are difficult to obtain and testing accidents are easily possible when 1.25 times related weight loads are tested for large tonnage cranes.

Hybrid partial least squares and neural network approach for short-term electrical load forecasting

Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares （PLS） and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.

Adaptive load balancing scheme in ad hoc networks

An adaptive load balancing scheme is proposed to balance the load in ad hoc networks. The new scheme can be applied in most on-demand routing protocols resulting in significant performance improvement. The proposed scheme is applied to the ad hoc on-demand distance vector （AODV） routing protocol. Simulation results show that the network load is balanced on the whole, and performance in packet loss rate, routing overhead and average end-to-end delay is also improved.

A Load Service System with Reputation Proof in P2P Networks

In this paper, a new system design for load services in computer networks with a new reputation system is constructed. The use of the reputation system is to address the free-rider problem. Database systems are used by directory agents to save information provided by load-server agents. Protocols are built for how a host finds available servers for load service or load transfer, especially when a host moves to a new region. Detailed procedures include how a directory agent builds its database, how a load-server agent provides services, and how a load-client agent receives its desired services. The system uses the fuzzy logic control method to transfer loads for load balancing, instead of the method of a fixed threshold level. For an ad-hoc (wireless) computer network framework, this new system structure is aimed to provide efficient ways for hosts to communicate with one another and to access resources in the system. This will also help the users of networks locate resources in a most effective and secure manner.