Multi-Granularity Autoformer for long-term deterministic and probabilistic power load forecasting.

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

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.

8 - Dynamic Electric Load Forecasting

Establishing a scientific and reasonable mid- and long-term power load forecasting method is the premise of power industry planning and construction. This paper constructs a hybrid electric load forecasting model based on Gaussian process (GPR) and particle swarm optimization (PSO). The paper uses the PSO algorithm to optimize the parameters in the co-variance function, and uses the modified parameters as the initial value to train the power load in the GPR model. Under the Bayesian framework, the parameters in the co-variance function are again optimized. Finally, the trained GPR model is used to predict the power load, and the results are compared with the auto-regressive integral moving average model and the exponential smoothing model. The verification results show that the hybrid electric load forecasting model based on Gaussian process (GPR) and particle swarm optimization (PSO) has good stability and higher prediction accuracy, and is suitable for medium and long-term electric load forecasting.

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.

This paper presents a novel approach for long-term electric power load forecasting. A three-layer back propagation(BP) network is designed using the artificial neural network. The idea is to forecast medium and long term power load of Shanxi Province using the ability of ANN of nonlinear modeling. Seven factors are selected as Input Variables for the proposed ANN. The seven factors include GDP, heavy industry production, light industry production, agriculture production, primary industry, secondary industry, tertiary industry. Variance contribution method new defined is used for the optimization selection of correlative factors, and forecasting accuracy is discussed. Simulation results show that the optimization selection of input variables of neural network model is feasible and effective.

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

To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed. This method divides data into administrative regions, industries, and load characteristics using a four-step method, extracts periodic features using Fourier transform, and uses Kmeans++ for clustering processing. On this basis, a Transformer model based on an adversarial adaptive mechanism is designed, which aligns the data distribution of the source domain and target domain through a domain discriminator and feature extractor, thereby reducing the impact of domain offset on prediction accuracy. The mean square error of the Fourier transform clustering method used in this study was 0.154, which was lower than other methods and had a better data denoising effect. In load forecasting, the mean square errors of the model in predicting long-term load, short-term load, and real-time load were 0.026, 0.107, and 0.107, respectively, all lower than the values of other comparative models. Therefore, the load forecasting model designed for research has accuracy and stability, and it can provide a foundation for the precise control of urban power systems. The contributions of this study include improving the accuracy and stability of the load forecasting model, which provides the basis for the precise control of urban power systems. The model tracks periodicity, short-term load stochasticity, and high-frequency fluctuations in long-term loads well, and possesses high accuracy in short-term, long-term, and real-time load forecasting.

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.

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

Power consumption forecasting is an important part of the macro planning of the industry and energy sector, and accurate forecasting of power load is very important for power grid management and power dispatching. At present, most of the power load forecasting takes the region as the object, but residents and small and medium-sized enterprise users are the basic units of electricity consumption, and their power load forecasting is as important as regional power load forecasting. compared with the regional power load, the electricity load of residents and small and medium-sized enterprises is more uncertain and more difficult to forecast. Therefore, this study combines the adaptive spectral clustering (ASC) method with the support vector quantile regression model (SVQR) to analyze the electricity consumption behavior of smart grid users and predict the residential power load. In this paper, the grid search is used to optimize the parameters of the Gaussian kernel SVQR model (GSVQR) to predict the power load, and compare it with other algorithms. From the two error evaluation index values of MAPE and pinball loss, the prediction effect of the GSVQR model is the best. In order to effectively provide uncertain information of power load, the GSVQR algorithm is used to predict the load of ultra-high energy consumption users and medium energy consumption users at any time in the future. Extensive experimental results show that: compared with other models, the prediction accuracy of the GSVQR model is higher; and the prediction results of the GSVQR model still have high reliability. Therefore, the method used in this paper can solve the problem of uncertainty of load forecasting.

With the booming development of the data center industry around the world, the power load forecasting of the data center plays an important role. In this paper, based on wavelet analysis and time series-based power load forecasting methods, data center power load forecasting is carried out. This paper uses the monthly power load data of a data center to carry out medium and long-term load forecasting, and achieves a good forecasting effect. This paper also uses daily load data to carry out short-term load forecasting, and uses a combination of one-step forecasting and time series-based methods for forecasting to achieve better forecasting results.

Efficient long-term electric load forecasting is vital for power system stability, yet traditional time series models often fall short in addressing complex trends and seasonal variations due to their reliance on fixed patterns and single-dimensional feature extraction. To overcome these limitations, this paper introduces the Learnable-DTimesNet-Linear model for enhanced load forecasting accuracy. The model leverages learnable decomposition to adaptively separate time series into seasonal and trend components. Seasonal sequences are processed with an enhanced TimesNet to capture periodicity, while trend sequences are modeled via a weighted summation using a linear model. This approach enables the model to adaptively capture subtle temporal fluctuations, improving predictive precision. Validation against six baseline models, including the original TimesNet, demonstrate the superiority of the proposed method, with a reduction in mean squared error by 10%–22%. These results underscore the Learnable-DTimesNet-Linear model's efficacy in handling complex time series data for accurate long-term electric load forecasting.

Long-term load forecasting and economical operation of wind farms for Egyptian electrical network

Medium-and long-term load forecasting in the distribution network has important guiding significance for overload warning of distribution transformer, transformation of distribution network and other scenarios. However, there are many constraints in the forecasting process. For example, there are many predict objects, the data sample size of a single predict object is small, and the long term load trend is not obvious. The forecasting method based on neural network is difficult to model due to lack of data, and the forecasting method based on time sequence law commonly used in engineering is highly subjective, which is not effective. Aiming at the above problems, this paper takes distribution transformer as the research object and proposes a medium-and long-term load forecasting method for group objects based on Image Representation Learning (IRL). Firstly, the data of distribution transformer is preprocessed in order to restore the load variation in natural state. And then, the load forecasting process is decoupled into two parts: the load trend forecasting of the next year and numerical forecasting of the load change rate. Secondly, the load images covering annual and inter-annual data change information are constructed. Meanwhile, an Image Representation Learning forecasting model based on convolutional neural network, which will use to predict the load development trend, is obtained by using load images for training; And according to the data shape, the group classification of the data in different periods are carried out to train the corresponding group objects forecasting model of each group. Based on the forecasting data and the load trend forecasting result, the group forecasting model corresponding to the forecasting data can be selected to realize the numerical forecasting of load change rate. Due to the large number of predict objects, this paper introduces the evaluation index of group forecasting to measure the forecasting effect of different methods. Finally, the experimental results show that, compared with the existing distribution transformer forecasting methods, the method proposed in this paper has a better overall forecasting effect, and provides a new idea and solution for the medium-and long-term intelligent load forecasting of the distribution network.