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Machine Learning Empowered electricity consumption prediction

Computers, Materials & Continua 2022年第7期72卷 1427-1444页

作者： Maissa A.Al Metrik Dhiaa A.Musleh Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

electricity,being the most efficient secondary energy,contributes for a larger proportion of overall energy *** to a lack of storage for energy resources,over supply will result in energy dissipation and substantial investment *** electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as:smart distributed grids,assessing the degree of socioeconomic growth,distributed system design,tariff plans,demand-side management,power generation planning,and providing electricity supply stability by balancing the amount of electricity produced and *** paper proposes amedium-termprediction model that can predict electricity consumption for a given location in Saudi ***,this study implemented a standalone ArtificialNeuralNetwork(ANN)model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi *** dataset used in this research is gathered exclusively from the Saudi Electric *** pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine *** number of neurons and learning rate of the standalone model were optimized using hyperparameter ***,the standalone model was tested against the bagging ensemble using the optimized *** bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN *** results for the proposed model produced 0.9116 Correlation Coefficient(CC),0.2836 Mean Absolute Percentage Error(MAPE),0.4578,Root Mean Squared Percentage Error(RMSPE),0.0298 MAE,and 0.069 Root Mean Squared Error(RMSE),respectively.

关键词： electricity consumption prediction artificial neural network machine learning

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Dynamic GM（1,1） Model Based on Cubic Spline for electricity consumption prediction in Smart Grid

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China Communications 2010年第4期7卷 83-88页

作者： WANG Xiaojia YANG Shanlin DING Jing WANG Haijiang Hefei University of Technology Hefei 230009 China

electricity demand forecasting plays an important role in smart grid expansion *** this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation *** piecewise polynomial interpolation thought,this model can dynamically predict the general trend of time series *** with low-order polynomial,the cubic spline interpolation has smaller error,avoids the Runge phenomenon of high-order polynomial,and has better approximation ***,prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying *** study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.

关键词： Smart Grid GM(1,1) Model Cubic Spline Rolling Strategy electricity consumption prediction

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Analysis and prediction of Regional electricity consumption Based on BP Neural Network

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Journal of Quantum Computing 2020年第1期2卷 25-32页

作者： Pingping Xia Aihua Xu Tong Lian Department of Information Engineering Jiangsu Maritime InstituteNanjing211170China Nanjing Institute of Science and Technology Information Nanjing210018China

electricity consumption forecasting is one of the most important tasks for power system workers,and plays an important role in regional power *** to the difference in the trend of power load and the past in the new normal,the influencing factors are more diversified,which makes it more difficult to predict the current electricity *** this paper,the grey system theory and BP neural network are combined to predict the annual electricity consumption in *** to the historical data of annual electricity consumption and the six factors affecting electricity consumption,the gray correlation analysis method is used to screen the important factors,and three factors with large correlation degree are selected as the input parameters of BP neural *** power forecasting model uses nearly 18 years of data to train and validate the *** results show that the gray correlation analysis and BP neural network method have higher accuracy in power consumption prediction,and the calculation is more convenient than traditional methods.

关键词： electricity consumption prediction BP neural network grey relational analysis

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A self-adaptive grey forecasting model and its application

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Journal of Systems Engineering and Electronics 2022年第3期33卷 665-673页

作者： TANG Xiaozhong XIE Naiming College of Economics and Management Nanjing University of Aeronautics and AstronauticsNanjing 211106China Department of Industry and Finance Huangshan Vocational and Technical CollegeHuangshan 245000China

GM(1,1)models have been widely used in various fields due to their high performance in time series ***,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some *** solve this problem,this paper proposes a self-adaptive GM(1,1)model,termed as SAGM(1,1)model,which aims to solve the defects of the existing GM(1,1)model family by deleting their modeling ***,a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed,the proposed multi-parameter optimization scheme adopts machine learning ideas,takes all adjustable parameters of SAGM(1,1)model as input variables,and trains it with firefly *** Sobol’sensitivity indices are applied to study global sensitivity of SAGM(1,1)model parameters,which provides an important reference for model parameter ***,forecasting capability of SAGM(1,1)model is illustrated by Anhui electricity consumption *** show that prediction accuracy of SAGM(1,1)model is significantly better than other models,and it is shown that the proposed approach enhances the prediction performance of GM(1,1)model significantly.

关键词： grey forecasting model GM(1,1)model firefly algo-rithm Sobol’sensitivity indices electricity consumption prediction

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