Changelogs » Eemeter

Eemeter

2.8.4

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* Further limit segments used in hourly `totals_metrics` to only calculate when weight=1.

2.8.3

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* Update hourly `totals_metrics` calculation to properly use only the segment of the model.

2.8.2

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* Add `totals_metrics` to hourly models.

2.8.1

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* Fix bug with `get_baseline_data` in regards to recent addition of `n_days_billing_period_overshoot` kwarg.

2.8.0

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* Update `get_baseline_data` to allow for limit to billing overshoot using `n_days_billing_period_overshoot` kwarg.

2.7.7

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* Add function to clean billing data to fit caltrack specifications (`clean_caltrack_billing_data`).

2.7.6

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* Update io functions to support latest pandas (>=0.24.x).
* Update documentation for CalTRACK Hourly methods.
* Add tutorial.

2.7.5

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* Fix completeness check for `get_terms` for last term.

2.7.4

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* Make more usable outputs for the `get_terms` function (list of eemeter.Term objects).

2.7.3

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* Update `as_freq` so it has an optional `include_coverage` parameter where it returns a dataframe with one column including the percent coverage of data used to create each sample.

2.7.2

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* Fixes the columns that are given in an empty prediction result called with the
` with_design_matrix=True` flag set for caltrack usage per day methods.
* Update bug report github issue template.
* Add test for `as_freq`.

2.7.1

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* Change `as_freq` to handle all Null series.

2.7.0

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* Add `get_terms` method to allow splitting reporting data into any number
of terms specified by day length.

2.6.0

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* Change `fit_caltrack_hourly_model` so it returns a `CalTRACKHourlyModelResults` object rather than a `CalTRACKHourlyModel`, in order to bring it in line with the `caltrack_usage_per_day` model outputs.

2.5.4post1

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* Update MANIFEST.in to fix release and update `./bump_version.sh` script
to remove build directories.

2.5.4

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* Add data fields to the `DataSufficiency` even if there are no warnings when calculating sufficiency.

2.5.3post2

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* Attempt 2 to fix release .whl file by removing local build and dist
directories before running `python setup.py upload`.

2.5.3post1

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* Fix release .whl file which had some extra directories.
* Add draft MAINTAINERS.md.

2.5.3

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* Fix `metered_savings` behavior so that it does not fail to compute error bands when there is 0 variance in the baseline.

2.5.2

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* Fix `as_freq` behavior to preserve sum and add a null last index at the target
frequency if necessary.

2.5.1

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* Capture an additional exception type (`KeyError`) in recently adjusted
`get_baseline_data` and `get_reporting_data` methods.

2.5.0

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* Add parameters to `get_baseline_data` and `get_reporting_data` to help make
these methods a bit more correct for billing data.
* Preserve nulls properly in `as_freq`.
* Update jupyter version to be compatible with latest tornado version.

2.4.0

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* Fix for bug that occasionally leads to `LinAlgError: SVD did not converge` error when fitting caltrack hourly models by addressing multi-collinearity when only a single occupancy mode is detected

2.3.1

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* Hot fix for bug that occasionally leads to `LinAlgError: SVD did not converge` error when fitting caltrack hourly models by converting the weights from `np.float64` ton `np.float32`.

2.3.0

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* Fix bug where the model prediction includes features in the last row that should be null.
* Fix in `transform.get_baseline_data` and `transform.get_reporting_data` to enable pulling a full year of data even with irregular billing periods

2.2.10

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* Added option in `transform.as_freq` to handle instantaneous data such as temperature and other weather variables.

2.2.9

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* Predict with empty formula now returns NaNs.

2.2.8

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* Update `compute_occupancy_feature` so it can handle instances where there are less than 168 values in the data.

2.2.7

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* SegmentModel becomes CalTRACKSegmentModel, which includes a hard-coded check that the same hours of week are in the model fit parameters and the prediction design matrix.

2.2.6

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* Reverts small data bug fix.

2.2.5

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* Fix bug with small data (1<week) for hourly occupancy feature calculation.
* Bump dev eeweather version.
* Add `bump_version` script.
* Filter two specific warnings when running tests:
statsmodels pandas .ix warning, and eemeter model fitting warning.

2.2.4

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* Add `json()` serialization for `SegmentModel` and `SegmentedModel`.

2.2.3

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* Change `max_value` to float so that it can be json serialized even if the input is int64s.

2.2.2

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* Add warning to `caltrack_sufficiency_criteria` regarding extreme values.

2.2.1

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* Fix bug in fractional savings uncertainty calculations using billing data.

2.2.0

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* Add fractional savings uncertainty to modeled savings derivatives.

2.1.8

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* Update so that models built with empty temperature data won't result in error.

2.1.7

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* Update so that models built from a single record won't result in error.

2.1.6

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* Update multiple places where `df.empty` is used and replaced with `df.dropna().empty`.
* Update documentation for running CalTRACK hourly methods.

2.1.5

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* Fix zero division error in metrics calculation for several metrics that
would otherwise cause division by zero errors in fsu_error_band calculation.

2.1.4

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* Fix zero division error in metrics calculation for series of length 1.

2.1.3

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* Fix bug related to caltrack billing design matrix creation during empty temperature traces.

2.1.2

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* Add automatic t-stat computation for metered savings error bands, the
implementation of which requires expicitly adding scipy to setup.py
requirements.
* Don't compute error bands if reporting period data is empty for metered
savings.

2.1.1

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* Fix degree day ranges (30-90) for prefab caltrack design matrix creation
methods.
* Fix the warning for total degree days to use total degree days instead of
average degree days.

2.1.0

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* Update the `use_billing_presets` option in `fit_caltrack_usage_per_day_model`
to use a minimum data sufficiency requirement for qualifying CandidateModels
(similar to daily methods).
* Add an error when attempting to use billing presets without passing a weights
column to facilitate weighted least squares.

2.0.5

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* Give better error for duplicated meter index in compute temperature features.

2.0.4

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* Change metrics input length error to warning.

2.0.3

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* Apply black code style for easy opinionated PEP 008 formatting
* Apply JSON-safe float conversion to all metrics.

2.0.2

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* Cont. fixing JSON representation of NaN values

2.0.1

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* Fixed JSON representation of model classes

2.0.0

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* Initial release of 2.x.x series