Combination approaches for short term load forecasting

Bichpuriya, Yogesh; Rao, M. Sreenivasulu; Soman, S. A.

doi:10.1109/ipecon.2010.5697079

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…The most usual way is the use of the mean, against which other alternatives are compared. The authors of [34] propose four approaches: the median, the use of weights proportional to the probability of success, the use of weights calculated by variance minimization, and the use of weights calculated from eigenvector of covariance matrix of forecast errors. Other alternatives are even more complex.…”

Section: Model Ensemblementioning

confidence: 99%

A Multi-Step Ensemble Approach for Energy Community Day-ahead Net Load Point and Probabilistic Forecasting

Ruano,

Ruano

2024

Preprint

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The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained by a small energy community of four houses located in the South region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

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Section: Model Ensemblementioning

confidence: 99%

A Multi-Step Ensemble Approach for Energy Community Day-ahead Net Load Point and Probabilistic Forecasting

Ruano,

Ruano

2024

Preprint

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“…The most usual way is the use of the mean, against which other alternatives are compared. The authors of [40] propose four approaches: the median, the use of weights proportional to the probability of success, the use of weights calculated by variance minimization, and the use of weights calculated from eigenvector of covariance matrix of forecast errors. Other alternatives are even more complex.…”

Section: Model Ensemblementioning

confidence: 99%

“…Several criteria can be used to assess the quality of the approximation. In this paper, the following will be employed: Root-Mean-Square of the Errors (RMSE) (36), Mean-Absolute Error (MAE) (37), Mean-Relative Error (MRE) (38), Mean-Absolute Percentage Error (MAPE) (39), and Coefficient of Determination, or R-Square (R 2 ) (40). Please notice that these criteria are computed with data normalized in the interval [−1, 1].…”

Section: Performance Criteriamentioning

confidence: 99%

Designing Robust Forecasting Ensembles of Data-Driven Models with a Multi-Objective Formulation: An Application to Home Energy Management Systems

Ruano

2023

Inventions

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This work proposes a procedure for the multi-objective design of a robust forecasting ensemble of data-driven models. Starting with a data-selection algorithm, a multi-objective genetic algorithm is then executed, performing topology and feature selection, as well as parameter estimation. From the set of non-dominated or preferential models, a smaller sub-set is chosen to form the ensemble. Prediction intervals for the ensemble are obtained using the covariance method. This procedure is illustrated in the design of four different models, required for energy management systems. Excellent results were obtained by this methodology, superseding the existing alternatives. Further research will incorporate a robustness criterion in MOGA, and will incorporate the prediction intervals in predictive control techniques.

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“…Expert based model which develops forecasting rules with help of utility has been discussed in [9]. The idea of combination of three different forecast models of expert systems, ANN and time series analysis to get a better performance than any one of the individual methods is explored in [8]. Numerous methods and models have already been implemented to solve the problem, yet there is scope for improvement.…”

Section: Introductionmentioning

confidence: 99%

Quick Learn Approach for load forecasting during COVID 19 lockdown

Lokhande

Soman

Hiremath

2020

2020 21st National Power Systems Conference (NPSC)

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be applied everywhere. If one model works well for a utility, it might not work well for other utility.Recently, the pandemic of COVID 19 has affected the entire world. At the initial stages, in order to suppress and mitigate the spread, the Government of India took a stringent step of imposing lockdown in the entire country which to led to a massive shutdown of all the industrial and commercial activities. This severely affected the load consumption profiles and decreased the consumption level. Since then, the load consumption patterns are changing because of continuously changing policies and regulations imposed by the government. The distribution utilities are facing challenges in their scheduling operations as the load forecasts obtained using conventional techniques are no longer accurate. They are suffering from huge economic losses because of bad forecasts.The lockdown measures in India started from 22 nd March 2020 when a curfew was announced for one day. The first phase of complete lockdown 'Lockdown 1.0' started on 24 th March 2020 and continued for 21 days till 14 th April 2020. The consumption pattern drastically changed during this period. Fig. 1: Load consumption profiles in Lockdown 1.0.We have considered an urban utility which has dominant industrial and commercial load than the residential load. The change in load of this utility is shown in 1. It can be seen that the load consumption suddenly decreased to a very low value from 22 nd March 2020. If we study the statistics of load in Table. I, average load during lockdown decreased by almost 46% while the peak decreased by 54%. If we observe Abstract-Load forecasting is extremely important for day to day operations and trading of any distribution utility. In recent times due to pandemic situation, the imposition of lockdown has caused all the commercial and industrial activities to shutdown. This has profoundly impacted the entire nation's electrical demand consumption. The consumption levels as well as the profiles have changed drastically. This has affected the forecast accuracy of the utilities exposing them to financial and technical risks. This paper presents and analyses the impact of COVID 19 on load consumption of a urban distribution utility. It also proposes a new forecasting model called 'Quick Learn Approach' (QLA) developed specifically for such lockdown period. Its main aim is to quickly learn and adapt to the constantly changing load consumption pattern while giving fairly accurate forecasts. The performance of the QLA model is compared with several other standard forecasting models so as to ensure its effectiveness.

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Combination approaches for short term load forecasting

Cited by 9 publications

References 16 publications

A Multi-Step Ensemble Approach for Energy Community Day-ahead Net Load Point and Probabilistic Forecasting

A Multi-Step Ensemble Approach for Energy Community Day-ahead Net Load Point and Probabilistic Forecasting

Designing Robust Forecasting Ensembles of Data-Driven Models with a Multi-Objective Formulation: An Application to Home Energy Management Systems

Quick Learn Approach for load forecasting during COVID 19 lockdown

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