Short Term Load Forecasting of Industrial Electricity Using Machine Learning

Porteiro, Rodrigo; Nesmachnow, Sergio; Hernández-Callejo, Luís

doi:10.1007/978-3-030-38889-8_12

Cited by 13 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This article proposes a novel mixed integer programming model for scheduling the deferrable electric appliances usage in households, which simultaneously considers minimizing the electricity cost and maximizing the users satisfaction. Users satisfaction measures to what extend the starting time and duration for appliances usage scheduled by the model match the users preferences-which is estimated through the analysis of historical data [4,5,22]. However, since this parameter can show certain variability between different days, a simulationoptimization resolution approach that considers this stochastic behaviour is devised.…”

Section: Introductionmentioning

confidence: 99%

A Simulation-Optimization Approach for the Household Energy Planning Problem Considering Uncertainty in Users Preferences

Rossit

Nesmachnow

Toutouh

et al. 2021

Production Research

Self Cite

View full text Add to dashboard Cite

Power supply is one of the basic needs in modern smart homes. Computer-aid tools help optimizing energy utilization, contributing to sustainable goals of modern societies. For this purpose, this article presents a mathematical formulation to the household energy planning problem and a specific resolution method to build schedules for using deferrable electric that can reduce the cost of the electricity bill while keeping user satisfaction at a satisfactory level. User satisfaction have a great variability, since it is based on human preferences, thus a stochastic simulation-optimization approach is applied for handling uncertainty in the optimization process. Results over instances based on real-world data show the competitiveness of the proposed approach, which is able to compute different compromise solution accounting for the trade-off between these two conflicting optimization criteria.

show abstract

Section: Introductionmentioning

confidence: 99%

A Simulation-Optimization Approach for the Household Energy Planning Problem Considering Uncertainty in Users Preferences

Rossit

Nesmachnow

Toutouh

et al. 2021

Production Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several methods have been proposed for the analysis of electricity utilization in residential and non-residential buildings [7,8]. The methods are classified into two main groups: intrusive and non-intrusive.…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive energy disaggregation by detecting similarities in consumption patterns

Chavat

Graneri

Nesmachnow

2020

redin

Self Cite

View full text Add to dashboard Cite

Breaking down the aggregated energy consumption into a detailed consumption per appliance is a crucial tool for energy efficiency in residential buildings. Non-intrusive load monitoring allows implementing this strategy using just a smart energy meter without installing extra hardware. The obtained information is critical to provide an accurate characterization of energy consumption in order to avoid an overload of the electric system, and also to elaborate special tariffs to reduce the electricity cost for users. This article presents an approach for energy consumption disaggregation in households, based on detecting similar consumption patterns from previously recorded labelled datasets. The experimental evaluation of the proposed method is performed over four different problem instances that model real household scenarios using data from an energy consumption repository. Experimental results are compared with twobuilt-in algorithms provided by the nilmtk framework (combinatorial optimization and factorial hidden Markov model). The proposed algorithm was able to achieve accurate results regarding standard prediction metrics. The accuracy was not affected in a significant manner by the presence of ambiguity between the energy consumption of different appliances or by the difference of consumption between training and test appliances.

show abstract

Electricity Demand Forecasting Using Computational Intelligence and High Performance Computing

Porteiro

Nesmachnow

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Short Term Load Forecasting of Industrial Electricity Using Machine Learning

Cited by 13 publications

References 18 publications

A Simulation-Optimization Approach for the Household Energy Planning Problem Considering Uncertainty in Users Preferences

A Simulation-Optimization Approach for the Household Energy Planning Problem Considering Uncertainty in Users Preferences

Non-intrusive energy disaggregation by detecting similarities in consumption patterns

Electricity Demand Forecasting Using Computational Intelligence and High Performance Computing

Contact Info

Product

Resources

About