k-Nearest Patterns for Electrical Demand Forecasting in Residential and Small Commercial Buildings

2021 ◽  
pp. 111396
Author(s):  
Meritxell Gomez-Omella ◽  
Iker Esnaola-Gonzalez ◽  
Susana Ferreiro ◽  
Basilio Sierra
Keyword(s):  
Demand Forecasting ◽  
Commercial Buildings ◽  
Electrical Demand

scholarly journals A hybrid approach to very small scale electrical demand forecasting

ISGT 2014 ◽  
2014 ◽  
Author(s):  
Andrei Marinescu ◽  
Colin Harris ◽  
Ivana Dusparic ◽  
Vinny Cahill ◽  
Siobhan Clarke
Keyword(s):  
Demand Forecasting ◽  
Hybrid Approach ◽  
Small Scale ◽  
Electrical Demand

Classification of Commercial Building Electrical Demand Profiles for Energy Storage Applications

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Anthony R. Florita ◽  
Larry J. Brackney ◽  
Todd P. Otanicar ◽  
Jeffrey Robertson
Keyword(s):  
United States ◽  
Energy Storage ◽  
The United States ◽  
Commercial Buildings ◽  
Discrete Wavelet ◽  
Commercial Building ◽  
Test Bed ◽  
Electrical Demand ◽  
The Impact

Commercial buildings have a significant impact on energy and the environment, being responsible for more than 18% of the annual primary energy consumption in the United States. Analyzing their electrical demand profiles is necessary for the assessment of supply-demand interactions and potential; of particular importance are supply- or demand-side energy storage assets and the value they bring to various stakeholders in the smart grid context. This research developed and applied unsupervised classification of commercial buildings according to their electrical demand profile. A Department of Energy (DOE) database was employed, containing electrical demand profiles representing the United States commercial building stock as detailed in the 2003 Commercial Buildings Consumption Survey (CBECS) and as modeled in the EnergyPlus building energy simulation tool. The essence of the approach was: (1) discrete wavelet transformation of the electrical demand profiles, (2) energy and entropy feature extraction (absolute and relative) from the wavelet levels at definitive time frames, and (3) Bayesian probabilistic hierarchical clustering of the features to classify the buildings in terms of similar patterns of electrical demand. The process yielded a categorized and more manageable set of representative electrical demand profiles, inference of the characteristics influencing supply-demand interactions, and a test bed for quantifying the impact of applying energy storage technologies.

Classification of Commercial Building Electrical Demand Profiles for Energy Storage Applications

2012 ◽  
Author(s):  
Anthony R. Florita ◽  
Larry J. Brackney ◽  
Todd P. Otanicar ◽  
Jeffrey Robertson
Keyword(s):  
United States ◽  
Energy Storage ◽  
The United States ◽  
Commercial Buildings ◽  
Department Of Energy ◽  
Discrete Wavelet ◽  
Commercial Building ◽  
Test Bed ◽  
Electrical Demand ◽  
The Impact

Commercial buildings have a significant impact on energy and the environment, utilizing more than 18% of the total primary energy consumption in the United States. Analyzing commercial building electrical demand profiles is crucial to understanding the relationships between buildings and the electrical grid for assessment of supply-demand interaction issues and potential; of particular importance are supply- or demand-side energy storage assets and the value they bring to various stake-holders in the Smart Grid context. This research develops and applies a systematic analysis framework to a Department of Energy (DOE) commercial building database containing electrical demand profiles representing the United States commercial building stock as specified by the 2003 Commercial Buildings Consumption Survey (CBECS) and as modeled in the Energy-Plus building energy simulation tool. The analysis procedure relies on three primary steps: 1) discrete wavelet transformation of the electrical demand profiles, 2) energy and entropy feature extraction from the wavelet scales, and 3) Bayesian probabilistic hierarchical clustering of the features to classify the buildings in terms of similar patterns of electrical demand. The process yields a categorized and more manageable set of representative electrical demand profiles, inference of the characteristics influencing supply-demand interactions, and a test bed for quantifying the impact of applying energy storage technologies.

scholarly journals Model Based on Artificial Neural Networks for Forecasting Electricity Consumption: A Holistic Approach

2020 ◽  
Author(s):  
Daniel Orlando Garzón Medina ◽  
Jose Calixto Lopes ◽  
Thales Sousa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Holistic Approach ◽  
Strategic Decisions ◽  
Maintenance Program ◽  
Artificial Neural ◽  
Government Data ◽  
Electrical Demand

Electrical demand forecasting is a key tool in making operational and strategic decisions in power companies, whose lack of accuracy can lead to high economic costs. In this sense, forecasting allows network operators to make power dispatch, maintenance program, reliability analysis and operational safety decisions. Therefore, the present work proposed the use of Artificial Neural Networks (ANN) to project the demand of the Colombian residential sector. The model presented for the forecast was based on socioeconomic variables obtained from official Colombian government data sources such as population growth, gross domestic product and residential electrical consumption. The work was developed with the aid of the MATLAB® software where a model with appreciable assertiveness margin were proposed.

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