Long-term Power Load Research Articles

Research on long term power load grey combination forecasting based on fuzzy support vector machine

Aiming at the problem of low accuracy of single model in power load forecasting, a method based on fuzzy support vector machine and grey combination forecasting is proposed. The power load data is analyzed and prepared by means of data repair, preprocessing and fuzzy C-means clustering, which is combined with fuzzy support vector machine and grey prediction. The clustering results are taken as the input of the grey prediction model, and the preliminary prediction results are obtained through parameter modification. Then, the results are taken as the input of support vector machine, and the long-term power load prediction is carried out by combining the two models. The experimental results show that the proposed method has excellent performance in predicting long-term power load accurately, and the prediction accuracy is significantly improved compared with the traditional single forecasting model. The application of this method to long-term power load forecasting below 4000 KWH proves its high prediction accuracy and reliability once again. When the clustering category of power load fuzzy clustering is set to 30, the best data clustering effect can be obtained, and the prediction accuracy is further improved.

Long-Term Power Load Forecasting Using LSTM-Informer with Ensemble Learning

Accurate power load forecasting can facilitate effective distribution of power and avoid wasting power so as to reduce costs. Power load is affected by many factors, so accurate forecasting is more difficult, and the current methods are mostly aimed at short-term power load forecasting problems. There is no good method for long-term power load forecasting problems. Aiming at this problem, this paper proposes an LSTM-Informer model based on ensemble learning to solve the long-term load forecasting problem. The bottom layer of the model uses the long short-term memory network (LSTM) model as a learner to capture the short-term time correlation of power load, and the top layer uses the Informer model to solve the long-term dependence problem of power load forecasting. In this way, the LSTM-Informer model can not only capture short-term time correlation but can also accurately predict long-term power load. In this paper, a one-year dataset of the distribution network in the city of Tetouan in northern Morocco was used for experiments, and the mean square error (MSE) and mean absolute error (MAE) were used as evaluation criteria. The long-term prediction of this model is 0.58 and 0.38 higher than that of the lstm model based on MSE and MAE. The experimental results show that the LSTM-Informer model based on ensemble learning has more advantages in long-term power load forecasting than the advanced baseline method.

A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting

Accurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the problems of insufficient feature extraction and a large volume of prediction models constructed according to seasons. Therefore, a hybrid feature pyramid CNN-LSTM model with seasonal inflection month correction for medium- and long-term power load forecasting is proposed. The model is constructed based on linear and nonlinear combination forecasting. With the aim to address the insufficient extraction of multiscale temporal correlation in load, a time series feature pyramid structure based on causal dilated convolution is proposed, and the accuracy of the model is improved by feature extraction and fusion of different scales. For the problem that the model volume of seasonal prediction is too large, a seasonal inflection monthly load correction strategy is proposed to construct a unified model to predict and correct the monthly load of the seasonal change inflection point, so as to improve the model’s ability to deal with seasonality. The model proposed in this paper is verified on the actual power data in Shaoxing City.

Load Forecasting Research of Markov Chain based on Data Modeling

The introduction of the electricity market plays an irreplaceable role in assisting the power industry to achieve stable operation and planning of the power grid, promoting the effective use and dispatch of electric energy, and avoiding the waste of resources. The role of load forecasting for the electricity market is self-evident, in the context of big data, by building a load forecasting model based on the Markov chain, and using the actual data of a certain region for simulation calculation. The results show that the Markov forecasting method can play a good role in medium and long-term power load forecasting, and can be used as a supplement to conventional load forecasting.

Medium- and long-term power load forecasting model

Power load is an important part of power system, and power load forecasting has an important impact on power system analysis, design and control. With the development of smart micro grid, load forecasting has gradually become an important module in the energy management system, It is “source, network, load and storage” “An important link in energy flow matching. The staged combined demand forecasting model of power grid based on neural network and polynomial regression is adopted, and judgment conditions are added to the neural network. If the training sample data does not converge in the neural network training process, the neural network forecasting is terminated, and the data is automatically transferred to the polynomial regression model to obtain the forecasting results. This method can be initially used for annual and monthly load forecasting. It is an intelligent micro grid The planning of has laid a certain technical foundation.

Multi-dimensional data-based medium- and long-term power-load forecasting using double-layer CatBoost

In this study, a medium- and long-term power load prediction method is proposed based on the two-layer categorical boosting (CatBoost) algorithm with multi-dimensional feature considerations. Simultaneously, the influences of economic fluctuation, power generation disruption, and meteorological data on power load are considered, whereby the dimension of power-load forecasting data characteristics is broadened. A randomised search cross-validation (CV) regression model is also applied to model parameter optimisation. Real data from a province in northeast China were used for the training and test sets. Compared with nine advanced load prediction models, including eXtreme gradient boosting and adaptive boosting, the coefficient of determination (R2) of the proposed method was 0.925, mean average percentage error (MAPE) was 0.0158, and root-mean-square error (RMSE) was 274.2036. In this study, a popular, viable artificial intelligence technology, two-layer CatBoost, was explored, and multi-dimensional external variables of power generation were added for the first time for load prediction. Finally, a higher accuracy load forecasting tree model is presented. The method has good potential for use in medium- and long-term power-load forecasting applications.

Short-term Power Load Forecasting Based on WNR-LSTM — Take the Singapore Region as an Example

In recent years, as the electrical energy of distributed storage has gradually increased, the randomness of load demand has increased. This makes it more difficult to rationally dispatch and store loads. How to quickly and accurately dig out more effective information and objective laws from the massive power load data, effectively clarify the instability and timing of load changes, and then reduce the consumption and accidents in dispatching is of great significance. This paper uses wavelet analysis and stochastic sparrow search algorithm to optimize LSTM neural networks to construct long- and short-term power load forecasting models, in Singapore as of December 2021 Empirical studies were conducted on the electricity load from 24 to January 23, 2022, as well as the corresponding peak-to-valley electricity prices, meteorological data and other related data. The results show that the long-term and short-term power load prediction model based on WNR-SSA-LSTM is compared with that of traditional LSTM In terms of neural networks, RMSE and MAPE were reduced by 4.000% and 1.7871%, model goodness-of-fit R2 increased by 0.0196, It is more suitable for short-term load forecasting.

Medium and Long-term Power Load Forecasting Based on Grey Markov Correction Model

Medium and long-term load forecasting is a necessary prerequisite for distribution network planning, and is of great significance for the improvement of economic and social benefits of the power system. Aiming at the defects of low prediction accuracy and poor applicability of traditional grey forecasting model, a medium and long-term load forecasting method of power system based on Markov residual correction and improved grey theory is proposed. Based on the classical grey forecasting GM(1,1) model, this method first divides the state according to the relative residuals, calculates the Markov residual transition matrix, and then uses the equal-dimensional innovation method to construct the grey Markov correction forecast. Model, and finally use residual processing method to revise the prediction results. The simulation results based on an actual example of load forecasting of annual power consumption in Guangxi Province show that compared with the traditional grey forecasting model, the grey Markov residual correction model proposed in this paper has significant advantages in forecasting accuracy and applicability.

Research on power load forecasting model of economic development zone based on neural network

The power load forecasting model can calculate the predicted value accurately and quickly, which will be helpful to distribute the power reasonably and improve the stability of the power grid. There are many uncertain factors involved in medium and long-term load forecasting, and the relationship between them is seriously nonlinear and dynamic, so the prediction accuracy of traditional methods is not high. However, dynamic regression neural network can display the dynamic characteristics of the power system more accurately. According to the results of power load characteristic analysis, the article discusses the application of Elman neural network in the medium-term and long-term power load forecasting of an economic development zone in Jiangsu Province, and realizes the complex mapping from the relevant historical power load to the forecasting target. The results show that this power load forecasting method is effective and practical.

Long-term load combination forecasting method considering the periodicity and trend of data

In order to solve the problems of insufficient accuracy of long-term power load forecasting and poor applicability of the model, this paper considers the coupling of a number of macro indicators, such as regional economic development and social development indicators, with the time series data of regional power load. BP neural network and Autoregressive integrated moving average model (ARIMA) are used to integrate and improve the forecasting model, so as to improve the trend forecasting ability of annual load forecasting model. The non parametric function method is used to forecast the periodic load data in the monthly load data, the annual load forecast is combined with the monthly load forecast to improve the overall forecasting accuracy of the model. Finally, through the comparison of grey prediction and other models and the verification of MAPE error analysis method, the prediction accuracy of the model method considering the combination of data periodicity and trend is significantly improved, which is suitable for the long-term prediction of regional power load.

A New Medium and Long-Term Power Load Forecasting Method Considering Policy Factors

At present, China’s power load development is facing a new situation in which policies such as the new economic norm, industrial structure adjustment, energy conservation and emission reduction, etc. are being deeply promoted, load growth in some areas begin to ease, and volatility of load gradually become prominent, which increases the difficulty of medium and long-term load forecasting. In this context, in view of multi-correlation, uncertainty of influence of policy factors on power load, in order to improve the accuracy of load forecasting under the influence of policy factors, and solve the problem that policy factors are ambiguous, difficult to be quantified, and difficult to be integrated into load forecasting model, a medium and long-term load forecasting model considering policy factors is proposed. First, by analyzing the influence of various policies on power load, a hierarchical policy influencing factor index system that combines macro and micro levels is constructed to systematically reflect the influence of economy and policies on load under the new situation. Then, in view of the traditional grey relational analysis model’s insufficient consideration of the difference of historical data and future power development situation, by respectively weighting historical periods and factor indexes, a quantification analysis model of power load influencing factors based on two-way weighted grey relational analysis is proposed to quantify the influence of various policy factors on power load, achieve the combination of subjective weighting and objective weighting,and obtain quantification weights. Finally, the weighted fuzzy cluster analysis method combined with weights is used to predict load under the influence of policy factors. The proposed model can better solve the difficulty of medium and long-term load forecasting caused by the volatility of load under the influence of policy factors, and is suitable for medium and long-term load forecasting under the background of policy changes. The analysis of calculation examples shows that compared with traditional grey relational analysis model, the quantification results of proposed methods are more realistic, compared with traditional prediction methods such as time series extrapolation and elasticity coefficient, proposed method has better prediction accuracy and engineering application value.

Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

Electric load forecast of long-period rail transit based on fuzzy mathematics

The urban rail transit power supply system is an important part of the urban power distribution network and the power source of the rail transit system. It is responsible for providing safe and reliable electrical energy to urban rail trains and power lighting equipment. This paper processes the obtained long-period rail transit power load learning sample data matrix, according to the principle of normalization processing, effectively eliminates irregular data in the sample set and fills in possible missing data, thereby eliminating bad data or fake data for model learning. Moreover, this avoids the generation of huge errors that cause exponential growth in the model due to the increase in the learning sample size and the irregularity of the data. According to the characteristics of power load, this paper comprehensively considers the influence of temperature and date type on the maximum daily load, applies the fuzzy neural network model to the long-period load forecasting of long-period rail transit, and introduces the whole process of establishing the forecasting model in detail. Through detailed analysis of the actual data provided by the EUNITE network, the relevant factors affecting the daily maximum load were determined, and then the appropriate fuzzy input was selected to establish the corresponding fuzzy neural network prediction model, and a relatively ideal prediction result was obtained. The experimental results fully proved the great potential of fuzzy neural network in long-term power load forecasting.

Application of Improved Grey Model in Medium and Long Term Load Forecasting

Grey model is a common method in medium and long-term power load forecasting, but it has great limitations. According to the characteristics of medium and long term power load forecasting, the method of sliding average and the principle of Markov model are introduced into the Grey forecasting theory, and the model is improved. This improvement can effectively improve the prediction accuracy of the model. Based on the load data of Qingdao City in the past ten years, it can be proved that the improved Grey model has improved the accuracy of the former.

Long-term load forecasting by a collaborative fuzzy-neural approach

Long-term power load forecasting is of major importance for power suppliers to define the future power consumption of a given region. However, it is not easy to contend with the uncertainty of the long-term load. In order to effectively forecast the long-term load, a collaborative principal component analysis and fuzzy feed-forward neural network (PCA-FFNN) approach is proposed in this study. The difference between this and existing methods is that the collaborative PCA-FFNN approach takes into account the different points of view in a more efficient way, and therefore the results obtained are more comprehensive and more in-depth. In the proposed methodology, a group of domain experts is formed. These domain experts are asked to configure their own PCA-FFNNs to forecast the long-term load based on their views. A collaboration mechanism is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, the partial-consensus fuzzy intersection and radial basis function network (PCFI-RBF) approach is used. The effectiveness of the proposed methodology is illustrated with a case study.

Application of Combination Forecast Model in the Medium and Long Term Power Load Forecast

For seasonal and long-term power load forecasting problem, this paper presents an optimal combination forecasting method, which can optimize the combination of multiple predictive models. Optimize the combination of the two model predictions with two models as an example, which are the gray GM(1,1) model and linear regression model, and finally compare the predicted values of combination with the real values. The results show that: the combination forecasting method has a high prediction accuracy, and the error is very small.

Application of Improved Grey Model in Long-term Load Forecasting of Power Engineering

Grey model is usually been used for long-term load forecasting in power engineering, but it has significant limitations. If the moving average method and Markov model are connected with grey model, the accuracy of this improved grey model used for long-term load forecasting in power engineering can be effectively increased. In this paper, ordinary grey model and improved grey model are all chosen and used for long-term power load forecasting in power engineering, and the power load data of Qingdao in the past decade is selected for the analysis. The result of the analysis shows that the accuracy of improved grey model is significant higher than ordinary model, so the improved grey model can be used for long-term load forecasting in power engineering.

Long-Term Power Load Forecast Based on Improved BP Neural Network

This article proposed the improvement BP algorithm which solved the neural network to restrain slow and easy well to fall into the partial minimum the question, through setup time sequence forecast model made the long-term power forecast, and made a comparison with the traditional BP natural network.

Parameter optimization of nonlinear grey Bernoulli model using particle swarm optimization

Nonlinear grey Bernoulli model (NGBM) is a novel grey forecasting model which is a simple modification of GM(1, 1) together with Bernoulli differential equation. This paper presents a new parameter optimization scheme of NGBM using the particle swarm optimization (PSO) algorithm. The power index of Bernoulli differential equation and production coefficient of the background value are considered as decision variables and the forecasting error is taken as the optimization objective. Parameter optimization of NGBM is formulated as the combinatorial optimization problem and would be solved collectively using PSO technique. Once the PSO finds the optimal parameters of NGBM, the model can be optimized. NGBM with this parameter optimization algorithm is then applied in long-term power load forecasting. Results show that NGBM has remarkably improved the forecasting accuracy and PSO is an effective global optimization algorithm suitable for the parameter optimization of NGBM.