electric-power-consumption

fedesoriano/electric-power-consumption is a Private Markets Signals data product published on Kaggle and indexed by The Alternative Data Encyclopedia.

Fedesoriano publishes electric-power-consumption as a Kaggle offering in the Private Markets Signals signal family. The listing has drawn 20,964 downloads and 169 likes on Kaggle.

The data

Time series analysis of power consumption

It is one of 20 listings in the Private Markets Signals family; between them the practical differences come down to coverage, history depth and how the raw signal is cleaned and delivered.

The source tags it with energy and electricity.

Structure, access and licensing

License: Data files © Original Authors. Size: 1427424. Last updated: 2022-08-01.

Access is through a dataset that is downloaded from Kaggle for use in a notebook. Its most recent recorded snapshot is from 2022-08-01.

The signal

Data on companies that are not publicly listed: fundraising by venture capital and buyout funds, deal activity, valuations, exits and portfolio company performance. The category aggregates voluntary disclosures from fund managers and regulatory filings into benchmarks for an asset class without centralized reporting.

Private valuations adjust slowly, so changes in round sizes, down-round frequency and extension rates foreshadow revisions to net asset values that public-market proxies price earlier. Fundraising momentum predicts deployment pressure in specific sectors, while secondary-market discount data reveals sentiment gaps between primary marks and clearing prices. Analysts also use hiring and web-traffic panels on portfolio companies to rank managers before fundraising closes. Core series include quarterly fundraising totals, capital called and distributed, deal counts and values by stage and geography, valuation marks of portfolio companies and exit activity through IPOs or acquisitions. Derived analytics track time-to-exit, dry powder, DPI-versus-TVPI dispersion across managers, and bridge rounds that signal portfolio distress.

Providers build databases from voluntary manager submissions, limited-partner disclosures, regulatory filings where available, and systematic collection from company announcements and press coverage. Records are entity-resolved to firms and funds, missing values are imputed with explicit methodology, and benchmarks aggregate into quartile statistics. Coverage audits compare known deals against captured ones to quantify completeness.

Caveats and compliance

Voluntary reporting biases samples toward larger, better-performing managers, inflating average returns in naive benchmarks. Valuation staleness smooths volatility and delays drawdown recognition, and backfilled histories overstate survivor performance. Deal values are frequently undisclosed, forcing imputation that varies by provider.

Much of the raw material arrives under confidentiality agreements that limit redistribution at name level, so vendors deliver aggregates and licensed extracts. Emerging private-fund disclosure regimes raise reporting burdens on managers and progressively enrich the compliant data layer.

Who uses this signal

Limited partners use benchmarks for manager selection and pacing models; funds-of-funds and consultants screen markets; secondary buyers price portfolios. Corporate development teams track competitor financing, and economists study startup formation from the same records.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading