For prediction from long-term historical data which Machine learning algorithm would be appropriate ? Initially I am thinking about LSTM but not confident about the possible outcome.
There is no best Machine Learning (ML) concept or algorithm for tasks. The choice of ML is affected by several factors such as: Data size, type, accuracy, complexity vs linearity, number of features, etc. First, you need have a good understanding of your data and what you want from the data. The data must be cleaned. Then carry out brief research to have a good understanding of what ML has been successfully applied on similar datasets like yours. This will give a brief idea of what ML to use.
Check these articles:
https://towardsdatascience.com/do-you-know-how-to-choose-the-right-machine-learning-algorithm-among-7-different-types-295d0b0c7f60
https://betterprogramming.pub/how-to-choose-the-machine-learning-algorithm-thats-right-for-you-652983383720
Alternatively, you can try WEKA, after loading the data, only algorithms that are suitable for data will be enabled.
WEKAS is a collection of machine learning algorithms for data mining tasks. It contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
For more information on WEKA check this link
https://www.cs.waikato.ac.nz/ml/weka/
However, with experience, knowledge of MLs and datasets, it is possible to guess which ML might be suitable for a particular task.
For different algorithms check these links:
https://heartbeat.fritz.ai/a-guide-to-the-top-machine-learning-algorithms-722d713b9b44
https://builtin.com/data-science/tour-top-10-algorithms-machine-learning-newbies
1) Artificial neural network is the most popular; outdoes statistical methods in forecasting energy demand.
2) Hybrid methods perform better than stand-alone ones in most cases.
3) Computational intelligence methods are suitable for short- to long-term forecasting.
4) Fuzzy and Grey prediction are suitable for forecasting with incomplete data sets.
5) Artificial neural network is the most widely used method, which is applied in 40% of the reviewed Energy planning models.
I like to suggest reading these two articles...
Article Forecasting methods in energy planning models
Article A review on renewable energy and electricity requirement for...
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