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Remove remains of the forecast feature (#645)
* Fix outputs - save data for investment year * Revert change to fix tests * Update results files * Restore test * Revert change to outputs cache * Get outputs for first year * Update mca.py * Remove foresight parameter (#641) * Move broadcast_techs out of quantities module * Update _inner_split * Fix trade model * Fix constraints tests * Fix demand_share tests * Fix quantities tests * Fix trade tests * Fix adhoc model * Rename broadcast_techs to broadcast_over_assets * Better docstring, rename argument * Delete filter_with_template * Remove `broadcast_over_assets` from `capacity_in_use` * Fix trade tests * Edit TODO * Remove foresight parameter * Update settings and documentation * Temporarily suppress test * Fix carbon budget framework * Revert change to settings * Update settings and documentation * Revert change to settings * Update results * Small changes to fix tests * Add warning about deprecated parameter * Clarify log messages * Better final message * Update results * Actually update results * And more * Add kwargs arguments to prevent complaints * Add more kwargs arguments * Update trade result * Remove more forecast features * Change docstring * Remove forecast settings * Simplify _interpolate * Update docs * Remove remaining references * Warning about deprecated parameter * Tidy run function
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docs/application-flow.rst

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Investment
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~~~~~~~~~~
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In the investment step is where new capacity is added to the different assets managed by the agents. This investment might be needed to cover an increase in demand (between now and forecast) or to match decommissioned assets, typically to do both.
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In the investment step is where new capacity is added to the different assets managed by the agents. This investment might be needed to cover an increase in demand (between now and the investment year) or to match decommissioned assets, typically to do both.
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The following graph summarises the process.
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First the demand is distributed among the available agents as requested by the ``demand_share`` argument of each ``subsector`` in the ``settings.toml`` file. This distribution can be done based on any attribute or property of the agents, as included in the ``Agents.csv`` file. Demand can also be shared across multiple agents, depending on the "quantity" attribute (defined in ``Agents.csv``). The two built-in options in MUSE are:
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- `standard_demand` (default): The demand is split only amongst *new* agents (indeed there will be an error if a *retro* agent is found for this subsector). *New* agents get a share of the increase in demand for the forecast years well as the demand that occurs from decommissioned assets.
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- `new_and_retro`: The input demand is split amongst both *new* and *retro* agents. *New* agents get a share of the increase in demand for the forecast year, whereas *retrofit* agents are assigned a share of the demand that occurs from decommissioned assets.
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- `standard_demand` (default): The demand is split only amongst *new* agents (indeed there will be an error if a *retro* agent is found for this subsector). *New* agents get a share of the increase in demand over the investment period as well as the demand that occurs from decommissioned assets.
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- `new_and_retro`: The input demand is split amongst both *new* and *retro* agents. *New* agents get a share of the increase in demand for the investment period, whereas *retrofit* agents are assigned a share of the demand that occurs from decommissioned assets.
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Then, each agent select the technologies it can invest in based on what is needed and the **search rules** defined for it in the ``Agents.csv`` file. The possible search rules are described in :py:mod:`muse.filters`. These determine the search rules for each replacement technology.
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docs/inputs/toml.rst

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- :py:func:`~muse.demand_share.standard_demand` (default): The input demand is
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split amongst *new* agents. *New* agents get a share of the increase in demand
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for the forecast years, as well as the demand that occurs from decommissioned
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over the investment period, as well as the demand that occurs from decommissioned
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assets.
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- :py:func:`~muse.demand_share.new_and_retro`: The input demand is split amongst
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both *new* and *retrofit* agents. *New* agents get a share of the increase in
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demand for the forecast year, whereas *retrofit* agents are assigned a share
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demand over the investment period, whereas *retrofit* agents are assigned a share
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of the demand that occurs from decommissioned assets.
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*constraints*
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macrodrivers_path = '{path}/technodata/Macrodrivers.csv'
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regression_path = '{path}/technodata/regressionparameters.csv'
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timeslices_levels = {'day': ['all-day']}
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forecast = [0, 5]
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The following attributes are accepted:
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docs/tutorial-code/add-agent/1-single-objective/settings.toml

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"demand_limiting_capacity"
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demand_share = "standard_demand" # Optional, default to standard_demand
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forecast = 5 # Optional, defaults to 5
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[sectors.power]
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type = 'default'

docs/tutorial-code/add-agent/2-multiple-objective/settings.toml

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demand_share = "standard_demand" # Optional, default to standard_demand
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forecast = 5 # Optional, defaults to 5
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docs/tutorial-code/add-correlation-demand/1-correlation/settings.toml

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demand_share = "standard_demand" # Optional, default to standard_demand
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forecast = 5 # Optional, defaults to 5
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docs/tutorial-code/add-new-technology/1-introduction/settings.toml

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demand_share = "standard_demand" # Optional, default to standard_demand
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forecast = 5 # Optional, defaults to 5
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docs/tutorial-code/add-new-technology/2-scenario/settings.toml

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demand_share = "standard_demand" # Optional, default to standard_demand
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forecast = 5 # Optional, defaults to 5
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docs/tutorial-code/add-region/1-new-region/settings.toml

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docs/tutorial-code/add-service-demand/1-exogenous-demand/settings.toml

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docs/tutorial-code/carbon-budget/1-carbon-budget/settings.toml

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