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

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.

In order to realize the modernization of power grid management, mid-term power load forecasting has become an important research in power dispatching. Accurate mid-term load forecasting is of great significance to future power system dispatching and safe operation, and is a prerequisite for safe and economical operation of power systems. Based on error back propagation (BP) neural network has the advantages of generalization ability, self-learning ability and self-adapting ability, and simple local calculation, this paper proposes to use the BP neural network model to forecast the power load in the alpine region. Firstly, the historical load data and meteorological data of a certain area in Harbin are studied, analyzed and screened, and they are used as the input data of the network, a mid-term load forecasting model based on BP neural network is established, and then the model is simulated and improved through simulation to predict changes in the future development of electric load demand. The prediction data results and images show that the medium-term load prediction accuracy and training speed based on the BP neural network can reach the target, and the performance is good.

Increasingly complex economic backgrounds and power consumption have created more difficulties for medium and long-term load forecasting. Traditional load forecasting models such as linear regression and time series models have a good effect on linear data, but the accuracy of nonlinear electric load data in Xinjiang is poor. The BP neural network model has better processing ability for nonlinear data, but it also has the problem of poor fitting and generalization ability. Aiming at this problem, this paper establishes a medium- and long-term power load forecasting model of Bayesian regularized BP neural network. Compared with the general gradient descent method BP neural network model, it is proved that the proposed model has a good effect on the electricity consumption forecast in Xinjiang.

Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.

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 first introduces modeling theory of GM (1,1) grey equidimensional filling vacancies, and establishes GM (1,1) grey forecasting model of equidimensional filling vacancies by using power demand load of Shan Dong Province from 2004 to 2011, then forecasts power load demand of Shan Dong Province in 2012 and 2013 for two years. When compared with the actual electricity load, that grey forecasting model of equidimensional idling vacancies has high accuracy in long-term power load forecasting.

Bus load forecasting plays an important role in terms of the safety of power grid dispatching and operation and the accuracy of online analysis and decision-making. In order to improve accuracy of bus load forecasting, this paper proposes an ultra-short-term load forecasting method based on multi-type data and hybrid model. Under the background of power big data, the multi-type data such as historical bus load data, date information and weather information are firstly taken as the input features of the prediction model, which is established based on the fusion of back propagation (BP-ANN) neural network and convolutional neural network(CNN). BP-ANN is used to extract the feature vectors of numeric type and category type data, which is concatenated with feature vectors extracted by CNN from image type data. The combined features are fed to a fully connected multilayer Neural Network to predict the bus load. In this paper, the historical data of active power load at the high voltage side of a 220kV substation in a certain area of China and the corresponding weather information are used for example analysis. The analysis of experimental results shows that the proposed method can make full use of the characteristics of multi-source data to conduct ultra-short-term bus load prediction, and it has higher load prediction accuracy compared to the single model prediction of BP-ANN and CNN.

Combining a neural network with a rule-based expert system approach for short-term power load forecasting in Taiwan

China is rich in rare earths (RE). However, the exploitation of rare earths not only promoted the social and economic development, but also produced negative environmental externalities, so the relevant departments issued policies and regulations to carry out pollution control work. This paper made a macroanalysis of China firstly. And then this paper took Jiangxi province as the main research area, which compared with Inner Mongolia, Guangdong, Sichuan and Fujian. This paper used the method of non-radial SBM to measure policy execution efficiency. Based on calculating the social and economic development indicator, economic development and social development indicator, this paper explored the feedback correlation of policy execution efficiency and the development of society and economy by grey relational analysis. The results showed that China reduced its dependence on rare earth resources. However, the efficiency of policy implementation needs to be improved. Effective implementation of rare earth industry policy has a feedback effect on social and economic development. From the perspective of case analysis, the implementation efficiency of vegetation destruction policy in Jiangxi province is higher than that of air pollution policy. The overall policy implementation efficiency of Jiangxi province has a strong feedback effect on the overall level of social and economic development, among which the air pollution and water pollution policy implementation efficiency has a relatively obvious feedback effect on social and economic development.

Regional cooperation plays a pivotal role in driving both regional and global economic growth, and the Belt and Road Initiative (BRI) stands as a significant catalyst for the economic and social development of the countries and regions along its routes. Based on economic and social data spanning from 2011 to 2019 across 40 countries, this research employs the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluation method to construct a Regional Economic and Social Development Index. Subsequently, employing a multi-period difference-in-differences model, the study meticulously examines the positive impact of the BRI on the development trajectory of relevant countries. Heterogeneity analysis reveals that the influence of the BRI is notably pronounced in developing nations, China's neighboring countries, and those along the Maritime Silk Road. Our research contributes significantly to elucidating the effects of regional cooperation on regional economic development, thus reinforcing the trajectory of economic globalization. Furthermore, we offer insightful policy recommendations for the future evolution of the Belt and Road Initiative.

In order to improve the accuracy of short-term load forecasting and solve the problems of time sequence, nonlinearity and multidimensionality of short-term power load data, this paper proposes a multi-step short-term power load forecasting method based on reinforcement learning Deep Q Network (DQN) and long-short-term memory network (LSTM). Firstly, the normalized historical data are sampled by time sliding window method to construct feature map as input. Then, LSTM is used as the main body to build a prediction model to capture long and short time series features. DQN is combined with LSTM network to form a skip layer to capture super-long time series features, so that DQN-LSTM can capture features of different time scales. Finally, the full-connection layer is introduced to fuse the multi-scale temporal features extracted from each layer, and the autoregressive model (AR) is used as the linear component to obtain the final prediction results. Experimental results verify that the MAPE and RMSE of the proposed prediction method are lower than those of other algorithms, and it can effectively capture the multi-scale time series features of power load data, thus improving the prediction accuracy.

15 - Forecasting of energy demand in virtual power plants

An essential element of electric utility resource planning is forecasting of the future load demand in the service area. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. In this paper, the development and testing of a hybrid intelligent long-term load forecasting system is presented consisting of several neural networks forecasting blocks, genetic algorithms for network architecture selection and optimization, and fuzzy rules for forecast combination. This is an application of increasingly significant importance to a deregulating electric utility industry. An overview of the current practice in long term load forecasting is presented, followed by an overview of the forecasting system design process utilized in generating the long-term load forecasts and a brief description of the key building blocks of the forecasting system. This is followed by some sample long-term forecasts performed for demonstrating the feasibility of the proposed approach.

In this paper, a PCA-SSA-DBIGRU-Attention multi-factor short-term power load forecasting model is proposed. Taking a complete account of the influence of meteorological factors, principal components analysis (PCA) is used to analyze the meteorological factors of daily minimum, maximum, daily average temperature, relative humidity, daily precipitation and power load data at the same time. The realization of original load data is dimensioned down. The complexity of power load forecasting models is reduced. Then, the Attention Double Bidirectional Gating Recurrent Unit (DBIGRU) model is constructed to calculate the different weights of the hidden layer states of the two-layer BIGRU. The hidden layer states are assigned different weights. The Sparrow Search Algorithm (SSA) is incorporated into the DBIGRU-Attention. The SSA-DBIGRU-Attention network model is constructed to optimize the learning rate, the number of iterations and the four hyperparameters of the first and second hidden layer neurons. The extracted principal components are input into SSA-DBIGRU-Attention to realize multi-factor short-term power load forecasting. Experimental results show that the prediction accuracy of the proposed model is improved, and the prediction time is reduced. Compared to the VMD-BILSTM, PCA-DBILSTM, CNN-GRU-Attention and CNN-BIGRU-Attention model, the four aspects of MAPE, MAE, RMSE and time are reduced by 29.55 %, 36.42 %, 32.34 % and 12.22 %, respectively, the R2 is improved by 3.09 %.

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series are introduced. In addition, multi neural network for load forecast including BP artificial neural network, RBF neural network and wavelet neural network are introduced, too. Then, a combination load forecasting model for power load based on chaotic time series, multiwavelet transformation and multi-neural networks is proposed and discussed in the paper. Firstly, the training sample is extracted from power load data through chaotic time series and multiwavelet decomposition. Then the obtained data is trained through BP network, RBF network and wavelet neural network. Lastly, the trained data from three neural networks are input a three-layer feedforward neural network based the variable weight combination load forecasting model. Simulation results show that accuracy of the combination load forecasting model proposed in the paper is higher than any one sole network model and the combination forecast model of three neural networks.