Explanatory and Causal Analysis of the MIBEL Electricity Market Spot Price

Machine learning applications for electricity market agent-based models: A systematic literature review

“…Goncalves et al [23] use multiple different methods to understand the main drivers of electricity prices. These include lasso and standard regression [79], and causal analysis such as Bayesian networks and classification trees.…”

“…Market Type Application Algorithm Used 2021 Fraunholz C. [19] International/National Price forecasting ANN 2021 Bouziane S.E. [7] Local energy market Forecasting carbon emissions ANN 2020 Babar M. [3] International/National Secure demand side management Naive Bayes classifier 2019 Maqbool A.S. [47] International/National Tariff design Linear regression 2019 Goncalves C. [23] International/National Price forecasting Linear regression, Lasso regression, Bayesian networks, Classification trees 2019 Pinto T. [60] International/National Risk management ANN 2019 El Bourakadi D. [14] Microgrid Bidding strategies Extreme Machine Learning 2016 Opalinski A. [57] International/National Demand forecasting Linear regression 2016 Pinto T. [62] International/National Price forecasting ANN, SVM [13] International/National Expansion planning Bi-level coordination optimization 2018 Gao Y.…”

Reviewing Explanatory Methodologies of Electricity Markets: An Application to the Iberian Market

“…Outside of regressions scopes, it is possible to find other methods for discovering the effects of explanatory variables in the electricity price formation. The most frequently used methods are simulation models [55][56][57], neural networks [58][59][60], principal component analysis [61], singular value decomposition [62], correlation methods [63,64], gradient boosting trees [65], copula models [66] and causal determination [67]. All simulation methods surveyed were agent-based models; they correspond to models in which the intervenor actions are modelled, and their aggregated interactions produce the result.…”

“…Overall, GBT creates thresholds within all variables, defining several paths and each path leads to a specific regression profile. With each of these paths, it is possible to better segment the electricity price to similar profiles and perform regressions on themselves [65]. Neural networks are a machine learning technique that corresponds to a set of layers that are composed of several nodes.…”

“…Using the merit order means that in MIBEL, price is a combination of the price defined by the producer and the emission cost of CO2 that will be generated to produce the electricity. In MIBEL, the average conversion rate to CO2 emissions across all units that use coal is 0.9 and 0.4 for gas [65]. This results in two new variables, coal_price and gas_price.…”