Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.

A comprehensive review for wind,solar,and electrical load forecasting methods

Wind power,solar power,and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand response load,the uncertainty on the production and load sides are both increased,bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy.Most review/survey papers focus on one specific forecasting object(wind,solar,or load),a few involve the above two or three objects,but the forecasting objects are surveyed separately.Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides.However,there is no corresponding review at present.Hence,our study provides a comprehensive review of wind,solar,and electrical load forecasting methods.Furthermore,the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript.Challenges and future research directions are discussed at last.

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.

Arduino-based low-cost electrical load tracking system with a long-range mesh network

A system that combines the advantage of the long-range(LoRa)communication method and the structural characteristics of a mesh network for an LoRa mesh network-based wireless electrical load tracking system is proposed.The system demonstrates considerable potential in reducing data loss due to environmental factors in farfield wireless energy monitoring.The proposed system can automatically control the function of each node by confirming the data source and eventually adjust the system structure according to real-time monitoring data without manual intervention.To further improve the sustainability of the system in outdoor environments,a standby equipment is designed to automatically ensure the normal operation of the system when the hardware of the base station fails.Our system is based on the Arduino board,which lowers the production cost and provides a simple manufacturing process.After conducting a long-term monitoring of a near-field smart manufacturing process in South Korea and the far-field energy consumption of rural households in Tanzania,we have proven that the system can be implemented in most regions,neither confined to a specific geographic location nor limited by the development of local infrastructure.This system comprises a smart framework that improves the quality of energy monitoring.Finally,the proposed big-data-technology-based power supply policy offers a new approach for prolonging the power supply time of off-grid power plants,thereby providing a guideline for more rural areas with limited power sources to utilize uninterrupted electricity.

Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power systems.In order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid platform.The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS.The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection.Besides,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions.Moreover,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm.The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.

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.

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

Precise Compound Control of Loading Force for Electric Load Simulator of Electric Power Steering Test Bench

Electric load simulator(ELS) systems are employed for electric power steering(EPS) test benches to load rack force by precise control. Precise ELS control is strongly influenced by nonlinear factors. When the steering motor rapidly rotates, extra force is directly superimposed on the original static loading error, which becomes one of the main sources of the final error. It is key to achieve ELS precise loading control for the entire EPS test bench. Therefore, a three-part compound control algorithm is proposed to improve the loading accuracy. First, a fuzzy proportional–integral plus feedforward controller with force feedback is presented. Second, a friction compensation algorithm is established to reduce the influence of friction. Then, the relationships between each quantity and the extra force are analyzed when the steering motor rapidly rotates, and a net torque feedforward compensation algorithm is proposed to eliminate the extra force. The compound control algorithm was verified through simulations and experiments. The results show that the tracking performance of the compound control algorithm satisfies the demands of engineering practice, and the extra force in the ELS system can be suppressed by the net torque corresponding to the actuator’s acceleration.

Deep learning for time series forecasting:The electric load case

Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging task.Recently,deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks,from image classification to machine translation.Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry,but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature.This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting,by contrasting deep learning architectures on short-term forecast(oneday-ahead prediction).Specifically,the focus is on feedforward and recurrent neural networks,sequence-to-sequence models and temporal convolutional neural networks along with architectural variants,which are known in the signal processing community but are novel to the load forecasting one.

Compression Sensibility of Magnetic-concentrated Fly Ash Mortar under Uniaxial Loading

The electrical conductivity, compression sensibility, workability and cost are factors that affect the application of conductive smart materials in civil structures. Consequently, the resistance and compression sensibility of magnetic-concentrated fly ash （MCFA） mortar were investigated using two electrode method, and the difference of compression sensibility between MCFA mortar and carbon fiber reinforced cement （CFRC） under uniaxial loading was studied. Factors affecting the compression sensibility of MCFA mortar, such as MCFA content, loading rate and stress cycles, were analyzed. Results show that fly ash with high content of Fe3O4 can be used to prepare conductive mortar since Fe3O4 is a kind of nonstoichiometric oxide and usually acts as semiconductor. MCFA mortar exhibits the same electrical conductivity to that of CFRC when the content of MCFA is more than 40% by weight of sample. The compression sensibility of mortar is improved with the increase of MCFA content and loading rate. The compression sensibility of MCFA mortar is reversible with the circling of loading. Results show that the application of MCFA in concrete not only provides excellent performances of electrical-functionality and workability, but also reduces the cost of conductive concrete.

A Dual-spline Approach to Load Error Repair in a HEMS Sensor Network

In a home energy management system(HEMS),appliances are becoming diversified and intelligent,so that certain simple maintenance work can be completed by appliances themselves.During the measurement,collection and transmission of electricity load data in a HEMS sensor network,however,problems can be caused on the data due to faulty sensing processes and/or lost links,etc.In order to ensure the quality of retrieved load data,different solutions have been presented,but suffered from low recognition rates and high complexity.In this paper,a validation and repair method is presented to detect potential failures and errors in a domestic energy management system,which can then recover determined load errors and losses.A Kernel Extreme Learning Machine(K-ELM)based model has been employed with a Radial Basis Function(RBF)and optimised parameters for verification and recognition;whilst a Dual-spline method is presented to repair missing load data.According to the experiment results,the method outperforms the traditional B-spline and Cubic-spline methods and can effectively deal with unexpected data losses and errors under variant loss rates in a practical home environment.

Clustering residential electricity load curve via community detection in network

Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the performances of previous widely-used LC clustering methods are poor in two folds:larger number of clusters,huge variances within a cluster(a CP is extracted from a cluster),bringing huge difficulty to understand the electricity consumption pattern of customers.In this paper,to improve the performance of LC clustering,a clustering framework incorporated with community detection is proposed.The framework includes three parts:network construction,community detection,and CP extraction.According to the cluster validity index(CVI),the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters.And the approach needs fewer clusters to achieve the same performance measured by CVI.

Study on Relationship between Summer Electricity and Meteorology in Changsha City

[ Objective] The research aimed to study relationship between summer electricity and meteorological factors. [ Method] Electrical load characteristics in Changsha during 2007 -2010 were analyzed. Correlation analysis between electrical load and meteorological factors （daily average temperature, the maximum temperature, the minimum temperature, rainfall, wind, relative humidity and atmospheric pressure） during July - September of 2007 -2010 was conducted. [ Result] Changes of the meteorological factors could directly affect electrical load, and temperature was the first influence factor. Prediction model of summer electrical load in Changsha was established by regression analysis method.[ Conclusion] The research could provide reference basis for prediction of the electrical load in Changsha.

Data-driven comparison of federated learning and model personalization for electric load forecasting

Residential short-term electric load forecasting is essential in modern decentralized power systems.Load forecasting methods mostly rely on neural networks and require access to private and sensitive electric load data for model training.Conventional neural network training aggregates all data on a centralized server to train one global model.However,the aggregation of user data introduces security and data privacy risks.In contrast,this study investigates the modern neural network training methods of federated learning and model personalization as potential solutions to security and data privacy problems.Within an extensive simulation approach,the investigated methods are compared to the conventional centralized method and a pre-trained baseline predictor to compare their respective performances.This study identifies that the underlying data structure of electric load data has a significant influence on the loss of a model.We therefore conclude that a comparison of loss distributions will in fact be considered a comparison of data structures,rather than a comparison of the model performance.As an alternative method of comparison of loss values,this study develops the"differential comparison".The method allows for the isolated comparison of model loss differences by only comparing the losses of two models generated by the same data sample to build a distribution of differences.The differential comparison method was then used to identify model personalization as the best performing model training method for load forecasting among all analyzed methods,with a superior performance in 59.1%of all cases.

Electric Load Clustering in Smart Grid:Methodologies,Applications,and Future Trends

With the increasingly widespread of advanced metering infrastructure,electric load clustering is becoming more essential for its great potential in analytics of consumers’energy consumption patterns and preference through data mining.Moreover,a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data,observe the characteristics of load clusters,and classify the components of the total load.This can give rise to the development of related techniques and research in the smart grid,such as demand-side response.This paper summarizes the basic concepts and the general process in electric load clustering.Several similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and disadvantages.Afterwards,eight indices widely used to evaluate the validity of electric load clustering are described.Finally,vital applications are discussed thoroughly along with future trends including the tariff design,anomaly detection,load forecasting,data security and big data,etc.

Modeling public holidays in load forecasting: a German case study

We address the issue of public or bank holidays in electricity load modeling and forecasting. Special characteristics of public holidays such as their classification into fixed-date and weekday holidays are discussed in detail. We present state-of-the-art techniques to deal with public holidays such as removing them from the data set,treating them as Sunday dummy or introducing separate holiday dummies. We analyze pros and cons of these approaches and provide a large load forecasting study for Germany that compares the techniques using standard performance and significance measures. Finally, we give general recommendations for the treatment of public holidays in energy forecasting to suggest how the drawbacks particular to most of the state-of-the-art methods can be mitigated. This is especially useful, as the incorporation of holiday effects can improve the forecasting accuracy during public holidays periods by more than 80%, but even for non-holidays periods, the forecasting error can be reduced by approximately 10%.

A Deep Neural Network Coordination Model for Electric Heating and Cooling Loads Based on IoT Data

As the ubiquitous electric power internet of things(UEPIoT)evolves and IoT data increases,traditional scheduling modes for load dispatch centers have yielded a variety of chal-lenges such as calculation of real-time optimization,extraction of time-varying characteristics and formulation of coordinated scheduling strategy for capacity optimization of electric heating and cooling loads.In this paper,a deep neural network coor-dination model for electric heating and cooling loads based on the situation awareness(SA)of thermostatically controlled loads(TCLs)is proposed.First,a sliding window is used to adaptively preprocess the IoT node data with uncertainty.According to personal thermal comfort(PTC)and peak shaving contribution(PSC),a dynamic model for loads is proposed;meanwhile,personalized behavior and consumer psychology are integrated into a flexible regulation model of TCLs.Then,a deep Q-network(DQN)-based approach,using the thermal comfort and electricity cost as the comprehensive reward function,is proposed to solve the sequential decision problem.Finally,the simulation model is designed to support the validity of the deep neural network coordination model for electric heating and cooling loads,by using UEPIoT intelligent dispatching system data.The case study demonstrates that the proposed method can efficiently manage coordination with large-scale electric heating and cooling loads.

An optimal hydropower contract load determination method considering both human and riverine ecosystem needs

In this research, a new method is developed to determine the optimal contract load for a hydropower reservoir, which is achieved by incorporating environ- mental flows into the determination process to increase hydropower revenues, while mitigating the negative impacts ofhydropower generation on riverine ecosystems. In this method, the degree of na^ral flow regime alteration is adopted as a constraint of hydropower generation to protect riverine ecosystems, and the maximization of mean annual revenue is set as the optimization objective. The contract load in each month and the associated reservoir operating parameters were simultaneously optimized by a genetic algorithm. The proposed method was applied to China＇s Wangkuai Reservoir to test its effectiveness. The new method offers two advantages over traditional studies. First, it takes into account both the economic benefits and the ecological needs of riverine systems, rather than only the economic benefits, as in previous methods. Second, although many measures have been established to mitigate the negative ecological impacts ofhydropower generation, few have been applied to the hydropower planning stage. Thus, since the contract load is an important planning parameter for hydropower generation, influencing both economic benefits and riverine ecosystem protection, this new method could provide guidelines for the establishment of river protection measures at the hydropower planning stage.