Combining forecasts in short term load forecasting: Empirical analysis and identification of robust forecaster

Bichpuriya, Yogesh; Soman, S. A.; Subramanyam, A.

doi:10.1007/s12046-016-0542-3

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…In this paper, we consider the worst-case scenario. [1] gives a comprehensive overview of current techniques for short term demand forecasting. They specifically investigate how combining forecasts obtained from an integrated auto-regressive moving average, an artificial neural network, and a similar day approach can improve the short term load forecast.…”

Section: Forecasting Errorsmentioning

confidence: 99%

“…Section 2.2.4) is shown. To do so, we assume the forecasting error for the demand to be d = 8% for every household [1], which could be obtained from a forecast performed by an Fig. 7 Peak-to-average ratio (PAR) reduction dependency on the participation rate.…”

Section: Influence Of Participation Rate and Forecasting Errorsmentioning

confidence: 99%

“…Our contributions are as follows: (1) We introduce a novel discrete time dynamic game for energy storage scheduling among prosumers in the smart grid. The closed form solution to the best-response problem is derived by means of a dynamic-programming approach.…”

mentioning

confidence: 99%

“…As of today, the forecasting module is not included into selma. For this study we assume this information to be given and refer the reader to[1,3] for details on demand forecasting.…”

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confidence: 99%

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A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors

Pilz

Al‐Fagih

2019

Dyn Games Appl

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Smart metering infrastructure allows for two-way communication and power transfer. Based on this promising technology, we propose a demand-side management (DSM) scheme for a residential neighbourhood of prosumers. Its core is a discrete time dynamic game to schedule individually owned home energy storage. The system model includes an advanced battery model, local generation of renewable energy, and forecasting errors for demand and generation.We derive a closed-form solution for the best-response problem of a player and construct an iterative algorithm to solve the game. Empirical analysis shows exponential convergence towards the Nash equilibrium. A comparison to a DSM scheme with a static game, reveals the advantages of the dynamic game approach. We provide an extensive analysis on the influence of the forecasting error on the outcome of the game. A key result demonstrates that our approach is robust even in the worst-case scenario. This grants considerable gains for the utility company organising the DSM scheme and its participants.

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Section: Forecasting Errorsmentioning

confidence: 99%

Section: Influence Of Participation Rate and Forecasting Errorsmentioning

confidence: 99%

mentioning

confidence: 99%

“…As of today, the forecasting module is not included into selma. For this study we assume this information to be given and refer the reader to[1,3] for details on demand forecasting.…”

mentioning

confidence: 99%

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A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors

Pilz

Al‐Fagih

2019

Dyn Games Appl

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“…The households follow these schedules, even if their actual demand differs from the forecasted one. Instead of using a forecasting algorithm, random errors were added to actual demands in order to simulate a realistic average error of 8% in individual forecasted data as reported in [33]. More details about the process used to simulate realistic forecasts can be found in Appendix A.…”

Section: A Scenariomentioning

confidence: 99%

Security attacks on smart grid scheduling and their defences: a game-theoretic approach

Pilz

Naeini

Grammont

et al. 2019

Int. J. Inf. Secur.

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The introduction of advanced communication infrastructure into the power grid raises a plethora of new opportunities to tackle climate change. This paper is concerned with the security of energy management systems which are expected to be implemented in the future smart grid. The existence of a novel class of false data injection attacks that are based on modifying forecasted demand data is demonstrated, and the impact of the attacks on a typical system's parameters is identified, using a simulated scenario. Monitoring strategies that the utility company may employ in order to detect the attacks are proposed and a game-theoretic approach is used to support the utility company's decision-making process for the allocation of their defence resources. Informed by these findings, a generic security game is devised and solved, revealing the existence of several Nash Equilibrium strategies. The practical outcomes of these results for the utility company are discussed in detail and a proposal is made, suggesting how the generic model may be applied to other scenarios.

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Load forecasting under data corruption based on anomaly detection and combined robust regression

Zheng

Jin

et al. 2019

Int Trans Electr Energ Syst

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Combining forecasts in short term load forecasting: Empirical analysis and identification of robust forecaster

Cited by 8 publications

References 14 publications

A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors

A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors

Security attacks on smart grid scheduling and their defences: a game-theoretic approach

Load forecasting under data corruption based on anomaly detection and combined robust regression

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