Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Mahnoor Khan, Nadeem Javaid, Sajjad, Abdullah, Adnan Naseem, Salman Ahmed, Muhammad Sajid Riaz, Mariam Akbar, Manzoor Ilahi

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Demand Response Management (DRM) is considered one of the crucial aspects of the
smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a
fascinating research area when numerous utility companies are involved and their announced prices
reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and
utility companies for efficient energy management. For this purpose, analytical consequences (unique
solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed
algorithm which converges for consumers and utilities. Moreover, different power consumption
activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load
forecasting is taken as the significant concerns in the power systems and energy management with
growing technology. The better precision of load forecasting minimizes the operational costs and
enhances the scheduling of the power system. The literature has discussed different techniques for
demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based
techniques. This paper presents a novel knowledge based system for short-term load forecasting.
The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge
based system. Besides, the proposed system has minimum operational time as compared to other
techniques used in the paper. Moreover, the precision of the proposed model is improved by a
different priority index to select similar days. The similarity in climate and date proximity are
considered all together in this index. Furthermore, the whole system is distributed in sub-systems
(regions) to measure the consequences of temperature. Additionally, the predicted load of the entire
system is evaluated by the combination of all predicted outcomes from all regions. The paper employs
the proposed knowledge based system on real time data. The proposed scheme is compared with
Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost.
In addition, the presented system outperforms other techniques used in the paper and also decreases
the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge
based system gives more efficient outcomes for demand load forecasting.
Original languageEnglish
Number of pages34
JournalElectronics
Volume7
Issue number12
DOIs
Publication statusPublished - 12 Dec 2018

Keywords

  • behavioral analytics
  • Stackelberg game
  • demand response
  • knowledge based systems
  • priority index
  • similar day
  • date proximity

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