## 4 Proposed Q-Learning Method for Smart Grid Price Forecasting
### Q-Learning-Based Electricity Price Forecasting
#### 4.2.3 Using \(Q\)-Learning for Dynamic Pricing Problem
Available online: [[https://www.utilitydive.com/news/5-countries-investing-heavily-in-smart-grid-tech/44875/](https://www.utilitydive.com/news/5-countries-investing-heavily-in-smart-grid-tech/44875/)]([https://www.utilitydive.com/news/5-countries-investing-heavily-in-smart-grid-tech/44875/](https://www.utilitydive.com/news/5-countries-investing-heavily-in-smart-grid-tech/44875/)) (accessed on 23 February 2022). * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Blockchain based balancing of electricity demand and supply. In _Lecture Notes in Networks and Systems_; Springer International Publishing: Cham, Switzerland, 2020; pp. 185-198. * [PERSON] and [PERSON] (2011) [PERSON]; [PERSON] Residential electricity load scheduling for multi-class appliances with Time-of-Use pricing. In Proceedings of the 2011 IEEE GLOBECOM Workshops (GC Wkshps), Houston, TX, USA, 5-9 December 2011; pp. 1194-1198. * [PERSON] (2012) [PERSON] Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy. _Energy_**2012**, _44_, 576-583. [CrossRef]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] A game-theoretic approach for optimal time-of-use electricity pricing. _IEEE Trans. Power Syst._**2013**, _28_, 884-892. [CrossRef][PERSON]; [PERSON]; [PERSON] Agent-based electricity market simulation with demand response from commercial buildings. _IEEE Trans. Smart Grid_**2011**, \(2\), 580-588. [CrossRef]
* _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON] A demand response energy management scheme for industrial facilities in smart grid. _IEEE Trans. Ind. Inform._**2014**, _10_, 2257-2269. [CrossRef]
* [PERSON] and [PERSON] (2014) [PERSON]; [PERSON] User-expected price-based demand response algorithm for a home-to-grid system. _Energy_**2014**, _64_, 437-449. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] A robust demand response control of commercial buildings for smart grid under load prediction uncertainty. _Energy_**2015**, _93_, 275-283. [CrossRef]
* _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] An automated residential demand response pilot experiment, based on day-ahead dynamic pricing. _Appl. Energy_**2015**, _155_, 195-203. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] A price-based demand response scheme for discrete manufacturing in smart grids. _Energies_**2016**, \(9\), 650. [CrossRef]
* [PERSON] and [PERSON] (2014) [PERSON]; [PERSON] Real-time dynamic pricing in a non-stationary environment using model-free reinforcement learning. _Omega_**2014**, _47_, 116-126. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Microgrid to enable optimal distributed energy retail and end-user demand response. _Appl. Energy_**2018**, _210_, 1321-1335. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep learning based ensemble approach for probabilistic wind power forecasting. _Appl. Energy_**2017**, _188_, 56-70. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. _Appl. Energy_**2018**, _221_, 386-405. [CrossRef]
* [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] Supply-demand balancing for power management in smart grid: A Stackelberg game approach. _Appl. Energy_**2016**, _164_, 702-710. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] Fairness-based packing of industrial IoT data in permissioned blockchains. _IEEE Trans. Ind. Inform._**2021**, _17_, 7639-7649. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Privacy-preserving and efficient multi-keyword search over encrypted data on blockchain. In Proceedings of the IEEE International Conference on Blockchain (Blockchain), Atlanta, GA, USA, 14-17 July 2019. * [PERSON] et al.