PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).

Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.

Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting

Monthly electricity consumption forecasting(ECF)plays an important role in power system operation and electricity market trading.Widespread popularity of smart meters enables collection of fine-grained load data,which provides an opportunity for improvement of monthly ECF accuracy.In this letter,a spatio-temporal granularity co-optimization-based monthly ECF framework is proposed,which aims to find an optimal combination of temporal granularity and spatial clusters to improve monthly ECF accuracy.The framework is formulated as a nested bi-layer optimization problem.A grid search method combined with a greedy clustering method is proposed to solve the optimization problem.Superiority of the proposed method has been verified on a real smart meter dataset.

A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption

Oil is an important strategic material and civil energy.Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize the allocation of social resources.Therefore,a new grey model FENBGM(1,1)is proposed to predict oil consumption in China.Firstly,the grey effect of the traditional GM(1,1)model was transformed into a quadratic equation.Four different parameters were introduced to improve the accuracy of the model,and the new initial conditions were designed by optimizing the initial values by weighted buffer operator.Combined with the reprocessing of the original data,the scheme eliminates the random disturbance effect,improves the stability of the system sequence,and can effectively extract the potential pattern of future development.Secondly,the cumulative order of the new model was optimized by fractional cumulative generation operation.At the same time,the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model,and the particle swarm optimization algorithm(PSO)was used to search the optimal parameters of the model to enhance the adaptability of the model.Based on the above improvements,the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results.Then,taking the petroleum consumption of China's manufacturing industry and transportation,storage and postal industry as an example,this paper verifies the validity of FENBGM(1,1)model,analyzes and forecasts China's crude oil consumption with several commonly used forecasting models,and uses FENBGM(1,1)model to forecast China's oil consumption in the next four years.The results show that FENBGM(1,1)model performs best in all cases.Finally,based on the prediction results of FENBGM(1,1)model,some reasonable suggestions are put forward for China's oil consumption planning.

Impact of residential building heating on natural gas consumption in the south of China:Taking Wuhan city as example

With the development of social economy,heating in the south of China has been concerned widely.As one of the energy sources of decentralized heating,natural gas(NG)has been used more and more popularly.This paper aimed to study the impact of residential building heating on NG consumption,and took Wuhan city,the representative city needing heating in winter of the south of China due to its location and climate,as an example.Firstly,a typical residential building model was established through DeST software.The heating load was simulated,and the corresponding NG consumption index was calculated.Secondly,appropriate methods were used to forecast the basic data of Wuhan city in 2020,including households and per capita gross national product(GDP),etc.Thirdly,the NG consumption of residential buildings with and without heating were predicted.Finally,the impact of residential building heating on NG consumption was analyzed.The results showed that the average annual household heating consumption of residential building in Wuhan city in 2020 was 2100 kWh/household,and the NG consumption using for residential building heating was 295 Nm 3/household.In addition,the NG consumption of residential building generated by space heating with 100%heating rate was 2.82 times the NG consumption generated by the stove and water heater,showing that residential building heating had a large impact on NG consumption.This study can contribute to choosing appropriate heating method in the southern cities of China,and further planning the gas pipe network in these cities.

Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone

In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach,this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone.First,three driving modes were developed for maize sowing scenarios:manual driving assisted driving and unmanned driving.While sowing,smartphone software and CAN(Controller Area Network)storage devices collected both positional data and engine operating conditions.Second,the tractor trajectory points were divided into kinematic sequences,with six driving cycle indicators built in each series based on the time window.Based on the semantic information of the kinematic sequences,the three operations of sowing,seeds filling,and turning round were well recognized.Last,a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm,regarding three factors:driving cycles,operating behaviors,and driving patterns.When compared to the traditional K-means algorithm,the results demonstrated that the harmonic mean of the precision and recall(F1 score)of sowing behavior recognition,seeds filling behavior recognition,and turning behavior recognition were enhanced by 2.06%,8.99%,and 21.79%,respectively.In terms of the impacts of driving modes and operating behaviors on fuel consumption,assisted driving mode had the lowest fuel usage for both sowing and turning behavior.Therefore,assisted driving is the most fuel-efficient mode for maize sowing.Combining the three driving modes,the relative error of the fuel consumption prediction model was 0.11 L/h,with the manual driving mode having the lowest relative error at 0.09 L/h.This research method lays the foundation for the optimization of tractor operation behavior,the selection of tractor driving mode,and the fine management of tractor fuel consumption.

Prediction of End-Use Energy Consumption in a Region of Northwest China

End-use energy consumption can reflect the industrial development of a country and the living standards of its residents. The study of end-use energy consumption can provide a solid basis for industrial restructuring, energy saving, and emission reduction. In this paper, we analyzed the end-use energy consumption of a region in Northwestern China, and applied the Markov prediction method to forecast the future demand of different types of end-use energy. This provides a reference for the energy structure optimization in the Northwestern China.

Quantitative Analysis and Prediction of China's Natural Gas Consumption in Different Sectors Based on Bayesian Network

In view of the heterogeneity of natural gas consumption in different sectors in China,this paper utilizes Bayesian network(BN)to study the driving factors of natural gas consumption in power generation,chemical and industrial fuel sectors.Combined with Bayesian model averaging(BMA)and scenario analysis,the gas consumption of the three sectors is predicted.The results show that the expansion of urbanization will promote the gas consumption of power generation.The optimization of industrial structure and the increase of industrial gas consumption will enhance the gas consumption of chemical sector.The decrease of energy intensity and the increase of gas consumption for power generation will promote the gas consumption of industrial fuel.Moreover,the direct influencing factors of gas price are urbanization,energy structure and energy intensity.The direct influencing factors of environmental governance intensity are gas price,urbanization,industrial structure,energy intensity and energy structure.In 2025,under the high development scenario,China’s gas consumption for power generation,chemical and industrial fuel sectors will be 66.034,36.552 and 109.414 billion cubic meters respectively.From 2021 to 2025,the average annual growth rates of gas consumption of the three sectors will be 4.82%,2.18%and 4.43%respectively.