A Two-Fold Machine Learning Approach for Efficient Day-Ahead Load Prediction at Hourly Granularity for NYC

Ahmed, Syed Shahbaaz; Thiruvengadam, Raghavendran; Karrthikeyaa, A. S. Shashank; Vijayaraghavan, Vineeth

doi:10.1007/978-3-030-12385-7_8

Cited by 3 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although no strict comparative study was conducted, it was found that the MAPE values, obtained for 24 h ahead, are competitive with some recently reported for the NYISO data set, [ 38 , 39 ]. The minimum accuracy was achieved for the MILLWD zone.…”

Section: Results and Discussioncontrasting

confidence: 60%

Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection

Krstonijević

2022

Sensors

View full text Add to dashboard Cite

For decentralized energy management in a smart grid, there is a need for electric load forecasting at different places in the grid hierarchy and for different levels of aggregation. Load forecasting functionality relies on the load time series prediction model, which provides accurate forecasts. Complex and heterogeneous multi-source load time series in a smart grid require flexible modeling approaches to meet the accuracy demand. This work proposes an adaptive load forecasting methodology based on the generalized additive model (GAM) with the big data estimation method. It is based on a set of GAM terms, constructed for a specific multi-source load forecasting application in the grid and a procedure that dynamically selects the most relevant terms and generates forecasts for particular load time series. Data from publicly available New York Independent System Operator (NYISO) databases are used for testing. The 24-hour-ahead forecasting results for eleven New York City zones, of different sizes and types, indicate the applicability of the proposed methodology.

show abstract

Section: Results and Discussioncontrasting

confidence: 60%

Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection

Krstonijević

2022

Sensors

View full text Add to dashboard Cite

show abstract

Short Term Load Forecasting of Industrial Electricity Using Machine Learning

2020

View full text Add to dashboard Cite

Electricity demand forecasting in industrial and residential facilities using ensemble machine learning

Porteiro¹,

Hernández-Callejo

Nesmachnow

2020

Rev.Fac.Ing.Univ.Antioquia

View full text Add to dashboard Cite

This article presents electricity demand forecasting models for industrial and residential facilities, developed using ensemble machine learning strategies. Short term electricity demand forecasting is beneficial for both consumers and suppliers, as it allows improving energy efficiency policies and the rational use of resources. Computational intelligence models are developed for day-ahead electricity demand forecasting. An ensemble strategy is applied to build the day-ahead forecasting model based on several one-hour models. Three steps of data preprocessing are carried out, including treating missing values, removing outliers, and standardization. Feature extraction is performed to reduce overfitting, reducing the training time and improving the accuracy. The best model is optimized using grid search strategies on hyperparameter space. Then, an ensemble of 24 instances is generated to build the complete day-ahead forecasting model. Considering the computational complexity of the applied techniques, they are developed and evaluated on the National Supercomputing Center (Cluster-UY), Uruguay. Three different real data sets are used for evaluation: an industrial park in Burgos (Spain), the total electricity demand for Uruguay, and demand from a distribution substation in Montevideo (Uruguay). Standard performance metrics are applied to evaluate the proposed models. The main results indicate that the best day ahead model based on ExtraTreesRegressor has a mean absolute percentage error of 2:55% on industrial data, 5:17% on total consumption data and 9:09% on substation data.

show abstract

A Two-Fold Machine Learning Approach for Efficient Day-Ahead Load Prediction at Hourly Granularity for NYC

Cited by 3 publications

References 6 publications

Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection

Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection

Short Term Load Forecasting of Industrial Electricity Using Machine Learning

Electricity demand forecasting in industrial and residential facilities using ensemble machine learning

Contact Info

Product

Resources

About