Investigating Periodic Dependencies to Improve Short-Term Load Forecasting

With a further increase in energy flexibility for customers,short-term load forecasting is essential to provide benchmarks for economic dispatch and real-time alerts in power grids.The electrical load series exhibit periodic patterns and share high associations with metrological data.However,current studies have merely focused on point-wise models and failed to sufficiently investigate the periodic patterns of load series,which hinders the further improvement of short-term load forecasting accuracy.Therefore,this paper improved Autoformer to extract the periodic patterns of load series and learn a representative feature from deep decomposition and reconstruction.In addition,a novel multi-factor attention mechanism was proposed to handle multi-source metrological and numerical weather prediction data and thus correct the forecasted electrical load.The paper also compared the proposed model with various competitive models.As the experimental results reveal,the proposed model outperforms the benchmark models and maintains stability on various types of load consumers.

Short-Term Power Load Forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.

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 Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.

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.

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.

Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System

Load forecasting is vitally important for electric industry in the deregulated economy. This paper aims to face the power crisis and to achieve energy security in Jordan. Our participation is localized in the southern parts of Jordan including, Ma’an, Karak and Aqaba. The available statistical data about the load of southern part of Jordan are supplied by electricity Distribution Company. Mathematical and statistical methods attempted to forecast future demand by determining trends of past results and use the trends to extrapolate the curve demand in the future.

Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents

According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.

A Weighted Combination Forecasting Model for Power Load Based on Forecasting Model Selection and Fuzzy Scale Joint Evaluation

To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.

Grid Power Optimization Based on Adapting Load Forecasting and Weather Forecasting for System Which Involves Wind Power Systems

This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load varying, and with the presence of the wind power systems on the grid, the grid will be more exposed to lowering its performance and increase the instability to other power systems on the gird. This is because of the intermittence behavior of the generated power from wind turbines as they depend on the wind speed which is varying all the time. However, with a good prediction of the wind speed, a close to the actual power of the wind can be determined. Furthermore, with knowing the load characteristics in advance, the new load curve can be determined after being subtracted from the wind power. Thus, with having the knowledge of the new load curve, and data that collected from SACADA system of the status of all power plants, the power optimization, load distribution and redistribution of the power flows between power plants can be successfully achieved. That is, the improvement of performance, more reliable, and more stable power grid.

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.

Rural Power System Load Forecast Based on Principal Component Analysis

Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.

A novel recurrent neural network forecasting model for power intelligence center

In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.

Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.

Short-Term Load Forecasting Using Radial Basis Function Neural Network

An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the short-term load of electric power system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical load data information of the Tai power system. The good agreements between the realistic values and forecasting values are obtained;the numerical results show that the proposed forecasting method is accurate and reliable.

Long-Term Electricity Demand Forecasting for Malaysia Using Artificial Neural Networks in the Presence of Input and Model Uncertainties

Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.

Prediction and Analysis on Short-term Load of Power System Based on LSTM

Effective short-term prediction of regional voltage load is of great significance to the implementation of energy saving and emission reduction policies in China.Accurate prediction of real-time demand voltage can reduce power waste and carbon emissions,make outstanding contributions to delaying global climate warming,and is conducive to global environmental protection and sustainable development.On the short-term load forecasting of power system,a variant model of RNN-LSTM is tested in this paper.It effectively solves the problem of gradient explosion and disappearance caused by large amount of data input in classical RNN.On the basis of this model,optimization experiments are carried out under different super parameters to achieve better prediction results.The experimental results show that the accuracy of test set reaches 99.8%,which proves that the method proposed in this paper has certain reference value.

LDformer:a parallel neural network model for long-term power forecasting

Accurate long-term power forecasting is important in the decision-making operation of the power grid and power consumption management of customers to ensure the power system’s reliable power supply and the grid economy’s reliable operation.However,most time-series forecasting models do not perform well in dealing with long-time-series prediction tasks with a large amount of data.To address this challenge,we propose a parallel time-series prediction model called LDformer.First,we combine Informer with long short-term memory(LSTM)to obtain deep representation abilities in the time series.Then,we propose a parallel encoder module to improve the robustness of the model and combine convolutional layers with an attention mechanism to avoid value redundancy in the attention mechanism.Finally,we propose a probabilistic sparse(ProbSparse)self-attention mechanism combined with UniDrop to reduce the computational overhead and mitigate the risk of losing some key connections in the sequence.Experimental results on five datasets show that LDformer outperforms the state-of-the-art methods for most of the cases when handling the different long-time-series prediction tasks.

Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.

基于误差幅空特性分析的空间负荷预测误差评价方法

对空间负荷预测误差进行有效评价是客观认识预测结果,指导预测结果合理应用的前提。然而,现有空间负荷预测误差评价的研究存在对误差的空间分布不考虑或考虑不充分导致评价不准确的问题。为此,提出一种基于误差幅空特性分析的空间负荷预测误差评价方法。首先,从空间负荷预测误差幅值大小和空间分布对电网规划影响的角度出发,对误差的幅空特性进行详细分析;其次,利用运输问题的数学模型来表征正负误差的幅空抵消特性,使用各空间邻近度–幅值误差值曲线与x轴围成面积之和来表征剩余未抵消误差的幅空叠加特性;然后,分别通过伏格尔法和各梯形面积累加公式来计算正负误差的幅空抵消影响值和剩余未抵消误差的幅空叠加影响值,并在此基础上构建空间负荷预测误差评价指标;最后,基于误差对电网规划的实际影响给出对误差评价指标性能的检验方法。算例分析表明,与传统方法相比,该文所提误差评价方法从幅值和空间两个维度实现了对空间负荷预测误差更为全面的评估,与误差对电网规划影响的实际情况贴近度更高。