• Mrs.S.Sri Saye lakshmi
• Jothishri R
• Neha Thamarai R
• B. Swetha

In the realm of electricity markets, ensuring fair energy distribution is paramount for efficiency, yet collusion among generation firms poses a significant threat. Our proposed method combats this by identifying potential collusion scenarios and computing market equilibriums, bolstering collusion detection. Unlike traditional supervised learning techniques, we leverage deep learning, specifically deep neural networks (DNNs) and recurrent neural networks (RNNs), to capture intricate market patterns more effectively. Our approach, evaluated on various test systems, exhibits superior collusion detection efficacy, highlighting the potency of deep learning in maintaining market transparency and fairness. This innovation equips Independent System Operators (ISOs) with a robust tool for energy distribution, as deep learning excels in discerning complex market dynamics. Overall, our findings herald the integration of deep learning as a significant stride in fortifying the resilience and equity of electricity markets.

Support vector machine, algorithms, deep neural networks and RNN

"ENHANCING FAIRNESS AND TRANSPARENCY IN ELECTRICITY MARKETS: DETECTING COLLUSION WITH MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 11, page no.65-73, November-2020, Available :http://www.jetir.org/papers/JETIR2011440.pdf

"ENHANCING FAIRNESS AND TRANSPARENCY IN ELECTRICITY MARKETS: DETECTING COLLUSION WITH MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp65-73, November-2020, Available at : http://www.jetir.org/papers/JETIR2011440.pdf