Adequacy of neural networks for wide-scale day-ahead load forecasts on buildings and distribution systems using smart meter data

Valgaev, Oleg; Kupzog, Friederich; Schmeck, Hartmut

doi:10.1186/s42162-020-00132-6

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Except for the influence factors, researchers are also very concerned about the forecast methods for improving forecast accuracy. The four main forecast method categories: time series models, econometric models, qualitative methods and artificial intelligence techniques are used in oil price modeling and forecasting (Wang et al, 2016;Charles & Darné, 2017;Yu et al, 2015;Sun et al, 2019;Suganthi & Samuel, 2012;Zhang et al, 2008;Valgaev et al, 2020). The autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS) are the most widely used time series forecasting model, and they are usually used as the benchmark models (Wang et al, 2018;Chai et al, 2018;Zhu et al, 2017).…”

Section: Forecast Methodsmentioning

confidence: 99%

Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model

Sun

Duan

et al. 2021

Energy Inform

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In recent years, the crude oil market has entered a new period of development and the core influence factors of crude oil have also been a change. Thus, we develop a new research framework for core influence factors selection and forecasting. Firstly, this paper assesses and selects core influence factors with the elastic-net regularized generalized linear Model (GLMNET), spike-slab lasso method, and Bayesian model average (BMA). Secondly, the new machine learning method long short-term Memory Network (LSTM) is developed for crude oil price forecasting. Then six different forecasting techniques, random walk (RW), autoregressive integrated moving average models (ARMA), elman neural Networks (ENN), ELM Neural Networks (EL), walvet neural networks (WNN) and generalized regression neural network Models (GRNN) were used to forecast the price. Finally, we compare and analyze the different results with root mean squared error (RMSE), mean absolute percentage error (MAPE), directional symmetry (DS). Our empirical results show that the variable selection-LSTM method outperforms the benchmark methods in both level and directional forecasting accuracy.

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Section: Forecast Methodsmentioning

confidence: 99%

Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model

Sun

Duan

et al. 2021

Energy Inform

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“…Simulation results show that the reliability of a single communication falls with the number of redundancies. [67] and [68] treated household separately and a forecast for 24 hours was done using SVM and neural network-based methods. The reasonable forecast was achieved through the technique applied.…”

Section: Operational Cost and Energy Usage Optimizationmentioning

confidence: 99%

Communication Network Simulation for Smart Metering Applications: A Review

Dahunsi¹,

Olayanju²,

Ponnle³

et al. 2021

Journal of Innovative Science and Engineering (JISE)

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Many countries are witnessing the deployment of millions of smart meters, as they strive to upgrade their traditional grid to the smart grid already prominent in the developed parts of the world. Communication networks that can offer high-speed communication will be required for transmission of the copious amount of data resulting from the numerous smart meters and other intelligent electronic devices (IEDs) in the distribution network substation. Therefore, there is a need to critically assess the available communication infrastructure for eventual upgrade and deployment. In this paper, a survey of the communication network and networks simulation software used for this purpose was carried out, with specific emphasis on smart metering applications. Some critical requirements identified are security, quality of service (QoS) and optimal cost for system operation. Also discussed are methods employed in the works of literature to handle the critical issues raised. For data transmission between the meters and data concentrators; power line communication (PLC) was mostly cost-effective and hence, mostly deployed. In the wireless category, ZigBee (802.15.4), and WiMAX (802.16) among others are suitable and have been successfully deployed. OPNET, NS-3, NS-2, and OMNET++ have been successfully employed for communication network simulations in smart metering applications.

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“…The International Organization for Metrology (OIML) established the TC3/SC4 working group to create relevant documents to measure whether private electricity meters can still be used. However, the faults of smart meters are diverse, and it is difficult for sampling verification methods to ensure that all types of faulty meters are tested without omission, which will inevitably cause losses to residents [8,9]. Some scholars tried to analyze the statistical data of smart meters to find the faults and operating errors of the energy meters.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm

Xie

Zhang

et al. 2021

Journal of Sensors

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This work is aimed at solving the morbidity problem of the smart meter fusion model and improve the measurement accuracy and reliability of the smart meter. Starting with the topology of the smart meter, the reason for the serious morbidity of the smart meter model is discussed. First, the basic process of power system state estimation of smart meters is introduced, and the concept of error analysis of smart meters is clarified. Then, the causes and mechanisms of the ill-conditioned problems of the smart meter model are analyzed, and methods to reduce the morbidity of the smart meter calculation model are analyzed. Finally, a data optimization algorithm based on a greedy strategy and an improved Tikhonov regularization method is proposed. The model data is processed and optimized to reduce the morbidity of the smart meter measurement model. The results show that the analysis algorithm for reducing the morbidity error of the smart meter proposed in this study can effectively interfere with the morbidity of the smart meter calculation model. The processing effect shows that it can reduce the measurement error of the smart meter to about 5%, which is an order of magnitude lower than the error before processing, and the processing effect of the least square method is improved by more than 70%. From the perspective of processing speed, when the user number is between 50 and 100, the running time of the algorithm ranges between 1.5 and 3.5 s, which can be fully adapted to the actual situation and has strong practicability. In short, this study is helpful in improving the accuracy and reliability of smart meter calculations and provides a certain reference for related research.

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Adequacy of neural networks for wide-scale day-ahead load forecasts on buildings and distribution systems using smart meter data

Cited by 4 publications

References 37 publications

Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model

Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model

Communication Network Simulation for Smart Metering Applications: A Review

Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm

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