A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction

Compared to the load characteristics of normal working days, weekend load characteristics have a low level of load and are sensitive to meteorological conditions, which influences the accuracy of short-term weekend-load forecasting. To solve this problem and to improve the accuracy of short-term weekend-load forecasting, a Semi-parametric weekend-load forecasting method based on the interaction between meteorological and load is proposed in this paper. The main work is shown as follows: (1) through separating weekend-load from normal-load and analyzing the correlation between meteorological factors and daily maximum load, the meteorological factors with parameter characteristics and non-parameter characteristics can be screened out; (2) a short-term weekend-load forecasting model is built according to Semi-parametric regression theory which can express the coupling relation between meteorology and load more realistically; (3) the effect of temperature accumulation is also considered to correct the forecasting model. The proposed method is proved by implementing short-term weekend-load forecasting on the real historical data of the Southern Power Grid in China. The result shows that the 96-point mean load forecasting accuracy obtained by this model can meet the requirement of power network operation.

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Short-term Load Forecasting Model Considering Meteorological Factors

E3S Web of Conferences ◽

10.1051/e3sconf/201911802050 ◽

2019 ◽

Vol 118 ◽

pp. 02050

Author(s):

Xi Yunhua ◽

Zhu Haojun ◽

Dong Nan

Keyword(s):

Decision Tree ◽

Meteorological Factors ◽

Load Forecasting ◽

Forecasting Model ◽

Analysis Method ◽

Decision Tree Algorithm ◽

Characteristic Analysis ◽

Short Term ◽

Tree Algorithm ◽

Short Term Load Forecasting

Because of the limitation of basic data and processing methods, the traditional load characteristic analysis method can not achieve user-level refined prediction. This paper builds a user-level short-term load forecasting model based on algorithms such as decision trees and neural networks in big data technology. Firstly, based on the grey relational analysis method, the influence of meteorological factors on load characteristics is quantitatively analyzed. The key factors are selected as input vectors of decision tree algorithm. This paper builds a category label for each daily load curve after clustering the user’s historical load data. The decision tree algorithm is used to establish classification rules and classify the days to be predicted. Finally, Elman neural network is used to predict the short-term load of a user, and the validity of the model is verified.

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Research on Regional Short-Term Power Load Forecasting Model and Case Analysis

Processes ◽

10.3390/pr9091617 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1617

Author(s):

Kang Qian ◽

Xinyi Wang ◽

Yue Yuan

Keyword(s):

Meteorological Factors ◽

Load Forecasting ◽

Weighted Average ◽

Support Vector ◽

Forecasting Model ◽

Electric Load ◽

Short Term ◽

Power Load ◽

Power Load Forecasting ◽

Electric Load Forecasting

Integrated energy services will have multiple values and far-reaching significance in promoting energy transformation and serving “carbon peak and carbon neutralization”. In order to balance the supply and demand of power system in integrated energy, it is necessary to establish a scientific model for power load forecasting. Different algorithms for short-term electric load forecasting considering meteorological factors are presented in this paper. The correlation between electric load and meteorological factors is first analyzed. After the principal component analysis (PCA) of meteorological factors and autocorrelation analysis of the electric load, the daily load forecasting model is established by optimal support vector machine (OPT-SVM), Elman neural network (ENN), as well as their combinations through linear weighted average, geometric weighted average, and harmonic weighted average method, respectively. Based on the actual data of an industrial park of Nantong in China, the prediction performance in the four seasons with the different models is evaluated. The main contribution of this paper is to compare the effectiveness of different models for short-term electric load forecasting and to give a guideline to build the proper methods for load forecasting.

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Short-Term Load Forecasting of Power System Based on Improved BP Neural Network

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2020.14.109 ◽

2020 ◽

Vol 14 ◽

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Power System ◽

Bp Neural Network ◽

Normal Load ◽

Load Forecasting ◽

System Optimization ◽

Forecasting Model ◽

Short Term ◽

Short Term Load Forecasting

Power system load is a stochastic and non-stationary process. Due to the influence of various factors, some bad data may exist in the load observation value. These data are mixed into the normal load data to participate in the training of neural network, which seriously affects the accuracy of load forecasting. Short-term load forecasting is the basis of power system operation and analysis, improving the precision of load forecasting is an important means to ensure the scientific decision-making of power system optimization. In order to improve the precision of short term load forecasting in power system, a short-term load forecasting model based on genetic algorithm is proposed to optimize BP neural network. Firstly, using genetic algorithm to optimize the initial weights and thresholds of BP neural network to improve the prediction accuracy of BP neural network; Through the comparison and analysis before and after the model optimization, the experimental results with smaller prediction error were obtained. The simulation results show that the short-term load forecasting model established by this method has faster convergence rate and higher prediction precision.

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Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

ACM Transactions on Internet Technology ◽

10.1145/3447513 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1-28

Author(s):

Song Deng ◽

Fulin Chen ◽

Xia Dong ◽

Guangwei Gao ◽

Xindong Wu

Keyword(s):

Gene Expression ◽

Probability Distribution ◽

Detection Rate ◽

Cross Validation ◽

Normal Load ◽

Gene Expression Programming ◽

Load Forecasting ◽

Short Term ◽

Forecasting Models ◽

Short Term Load Forecasting

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

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