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In the modern day world and with growing technology, load forecasting is taken as the significant concerns in the power systems and energy management. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has proposed different techniques for demand load forecasting like neural networks, fuzzy methods, Na ¨ ıve Bayes and regression based techniques. This paper proposes a novel knowledge based system for short-term load forecasting. 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. Besides, 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 model is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the proposed system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on yearly basis. Furthermore, the proposed knowledge based system gives more efficient outcomes for demand load forecasting.

International Conference on Cyber Security and Computer Science
ICONCS

Mahnoor Khan Nadeem Javaid Yüksel Çelik Asma Rafique