Short-Term Power Load Forecasting Based on Spark Platform and Improved Parallel Ridge Regression Algorithm

Du, Dajun; Xie, Jing; Fu, Zhiyuan

doi:10.23919/chicc.2018.8483383

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Ridge regression is commonly used when independent variables are highly correlated and is used in load forecasting as a comparison [52] or in improved versions [53]. Bayesian ridge is a special case of Bayesian linear regression, where the mean of a variable is a linear combination of other variables, and the posterior distribution can be approximated.…”

Section: Energy Consumption Forecasting Methodsmentioning

confidence: 99%

Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

et al. 2022

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Industry 4.0 and its technologies allow advancements in communications, production and management efficiency across several segments. In smart grids, essential parts of smart cities, smart meters act as IoT devices that can gather data and help the management of the sustainable energy matrix, a challenge that is faced worldwide. This work aims to use smart meter data and household features data to seek the most appropriate methods of energy consumption prediction. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, Python Platform, and several prediction methods, prediction experiments were performed with household feature data and past consumption data of over 470 smart meters that gathered data for three years. Support vector machines, random forest regression, and neural networks were the best prediction methods among the ones tested in the sample. The results help utilities (companies that maintain the infrastructure for public services) to offer better contracts to new households and to manage their smart grid infrastructure based on the forecasted demand.

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Section: Energy Consumption Forecasting Methodsmentioning

confidence: 99%

Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

et al. 2022

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“…The Spark platform is used to divide all the obtained data and compute them in parallel to speed up the processing of big data. First, the data is pre-processed through feature extraction, and the input that meets the requirements of the model is obtained, which input into the multivariate L2-Boosting for training and learning and get the final regression model [5]. The grey prediction method is also a common method of load prediction, which added secondary smoothing processing through historical data to eliminate the interference factors of historical data with Markov chain and grey theory to predict the residual sequence and the sign of the future residual together to revise the results [6].…”

Section: Load Feature Extractionmentioning

confidence: 99%

“…It can also be learned by sharing representations between multiple modalities to further improve the accuracy index on specific tasks. Researchers have begun to carry out research in various fields for multi-modal model, such as multi-modal model based on fuzzy cognitive maps [5], which first extract a subset from the complete data and trained separately on each subset, then used fuzzy cognitive maps for modelling and prediction, and finally the output was fused from each subset by the information granulation.…”

Section: Multi-modal Deep Learningmentioning

confidence: 99%

A Multi-modal Time Series Intelligent Prediction Model

Xian,

Liang

2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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The power load prediction can ensure the power supply and dispatch, which will be useful for market participants to plan and make strategic decisions to enhance reliability, save operation and maintenance costs. Short-term load data series have obvious approximate periodicity, while long-term load data series show variability and dynamic features. In addition, time series data of various modalities, such as market reports and production management data, could play a role in load prediction. One kind of multi-modal CNN-BiLSTM architecture is proposed to predict short-term and long-term load data, which have an improved shared parameter convolutional network to learn feature representation and an improved attention-based BiLSTM mechanism, which could model the dynamic features of multimodal on time series data. Experimental results on multimodal dataset show that, compared with other baseline systems, this model has some advantages in the prediction accuracy.

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