I'm new to this forum and I'm quite new to forecasting.
Currently I'm trying to learn the basics about exponential smoothing, ARIMA etc. Now I want to forecast the total energy consumption of a rather small company at the end of the month. You can see a typical power load profile for a full week in this image.
The consumed energy of a month would be the integral over the entire load profile of a month. The weekdays have a strong correlation but the weekend is totally different. I have historical data for more than a year. Also there might be a small long term upwards trend. Every month the goal is to forecast the energy at the end of the month by means of the already passed days in this month and any data from previous (complete) months.
My question: Which forecasting method(s) do you consider suitable for such a case? Which methods I should study?
I should have stated that this is a research project for a software development. We have a software for data aquisition from proprietary measuring devices and a new software for visualization that we would like to equip with some forecasting functionalities.
During the coming year we want to investigate several forecasting methods/possibilities and learn as much as possible. The quality or accurateness is not the primary goal at the moment. At a later time we may surely want to use a product like Autobox. Currently we would like use R for analysis and try to implement some things in the software ourselves, most likely by using open source libraries for the algorithms.
It's not the goal to have the best solution from the start on but rather to encounter the shortcomings of easier methods and advance to better solutions.
So I should maybe rephrase my question: What are the forecasting methods to start with for these data? In which direction should we go? Should we handle weekdays and weekends completely separate?