Solution for long-term electricity prices forecasting I'm trying to adopt a solution for long-term electricity annual prices forecasting (depending on past electricity prices, past oil prices, past consumption data, etc.)
I'm considering some solutions:


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*off-the-shelf specific software: Alyuda, Aleasoft, ...

*already-built modules of known software (Excel, MatLab, R): neuroXL for Excel, modules for Simulink, ...

*code from scratch (Python, R) by using known models and Machine Learning: scikit-learn, Weka, regression, ...


I would like to find more solutions to test and, if possible, some experiences using them. 
 A: Electricity price forecasting is a very active field of research. I would recommend you take a look at recent papers. Off the top of my head, two recent reviews are Nowotarski & Weron (2018) and Weron (2014). The electricity load forecasting literature may also be inspirational, e.g., Hong et al. (2016) or Hong et al. (2019) or Hong & Pinson (2020). More generally, Rafał Weron is probably the most knowledgeable person about electricity price forecasting, and Tao Hong and Pierre Pinson for load forecasting in the world, so it would make sense to look at their recent publications and follow them.
However, note that most time series forecasting work is short-term. If you are interested in long term price forecasting, then political factors will be far more important: what energy mix will be supported by government, will there be price controls, CO2 emission rights, will these be traded or not, will countries be able to build the necessary high voltage power lines to bring electricity from places where it can be generated (offshore wind parks, desert solar plants) to where it is used, etc. So a well crafted scenario analysis will be far more useful than a complicated deep learning network. (Less impressive, perhaps.)
