CHANGELOG.md

Changelog

Development

• Clarify blackout period.

2.8.6

• Fix issue with get_reporting_data and get_baseline_data when passing data with non-UTC timezones.

2.8.5

• Add functions to clean billing/daily data according to caltrack rules.

2.8.4

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

2.8.3

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

2.8.2

• Add totals_metrics to hourly models.

2.8.1

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

2.8.0

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

2.7.7

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

2.7.6

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

2.7.5

• Fix completeness check for get_terms for last term.

2.7.4

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

2.7.3

• 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

• 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

• Change as_freq to handle all Null series.

2.7.0

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

2.6.0

• 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.

2.5.4-post1

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

2.5.4

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

2.5.3-post2

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

2.5.3-post1

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

2.5.3

• 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

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

2.5.1

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

2.5.0

• 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

• 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

• 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

• 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

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

2.2.9

• Predict with empty formula now returns NaNs.

2.2.8

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

2.2.7

• 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

• Reverts small data bug fix.

2.2.5

• 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

• Add json() serialization for SegmentModel and SegmentedModel.

2.2.3

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

2.2.2

• Add warning to caltrack_sufficiency_criteria regarding extreme values.

2.2.1

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

2.2.0

• Add fractional savings uncertainty to modeled savings derivatives.

2.1.8

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

2.1.7

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

2.1.6

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

2.1.5

• 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

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

2.1.3

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

2.1.2

• 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

• 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

• 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

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

2.0.4

• Change metrics input length error to warning.

2.0.3

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

2.0.2

• Cont. fixing JSON representation of NaN values

2.0.1

• Fixed JSON representation of model classes

2.0.0

• Initial release of 2.x.x series