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Massachusetts Data Center Intel
4 verified signals across 1 counties tracked daily.
Massachusetts · Construction & power moves · 1
full tracker →Land, power, and interconnection moves across Massachusetts — each traced to primary filings.
Counties
| County | Last 7d | Total |
|---|---|---|
| Hampshire County | 0 | 1 |
Top JUST IN — Massachusetts
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Gaussian Mixture Model (GMM) Update: Incorporating Massachusetts Hub Electricity Futures into Day-Ahead Ancillary Services (DAAS) Forecasted Real-Time Energy Prices
Source: ISO New England Inc. · Jul 22, 2026ISO New England Inc. says it has “recently filed a targeted set of changes to the DA A/S market including adjustments to Forecast Energy Requirement Demand Quantity (FER DQ) and the introduction of a strike price floor,” and that these changes “are intended to address inefficiencies in the DA A/S market identified by the IMM.” The memo also says ISO-NE is “planning to use Massachusetts Hub day-ahead energy futures prices from Intercontinental Exchange” to update its real-time price forecasts, a technical market-design change in Massachusetts.
Backed by 1 primary filing — sign in or book a call to see all sources.
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Abeyance Request and Section 205 Filing: New England Large and Co-Located Loads Show Cause Order (EL26-72)
Source: ISO New England · Jul 16, 2026ISO New England says FERC opened a Section 206 proceeding and preliminarily found ISO-NE’s tariff “unjust, unreasonable, or unduly discriminatory or preferential due to a lack of large and co-located load integration provisions.” ISO-NE and the PTOs plan to seek a 90-day abeyance by August 3, 2026, which would let them file Section 205 tariff changes on November 16, 2026 to address FERC’s five reform categories, including cost transparency, co-location rules, flexible load service, and generation studies.
Backed by 1 primary filing — sign in or book a call to see all sources.
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ISO-NE System Forecasting Public Webinar – 2026 CELT Overview
Source: ISO New England · Jun 24, 2026ISO New England’s June 24, 2026 public webinar on the 2026 CELT report says large loads, including data centers, are now part of its demand modeling: it added a “large loads forecast,” notes “Data Centers,” and says “Only a few projects in formal study (<300 MW).” ISO-NE also says the near-term impact is “Minimal” before 2027-2028, with a longer-term “~110–130 MW impact to peak demand by 2030s–2040s.”
Backed by 1 primary filing — sign in or book a call to see all sources.
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Consumer Liaison Group Meeting Summary
Source: ISO New England · Jun 02, 2026ISO New England’s June 2, 2026 Consumer Liaison Group meeting summary says the 2026 CELT forecast now includes “new methodologies to forecast the growth in load driven by data centers,” while noting that “near-term impacts remain relatively modest” and that there is “uncertainty” about future projects (ISO New England). The summary also highlights large-load risks including “infrastructure constraints,” “timing mismatches between load growth and system upgrades,” and “increased reliance on pipeline infrastructure,” and says data-center costs should be borne “primarily by those facilities rather than existing ratepayers” (ISO New England).
Backed by 1 primary filing — sign in or book a call to see all sources.
Recent Massachusetts data center news
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Startup accelerates progress toward light-speed computing
Lightmatter, co-founded by MIT alumni including Nicholas Harris, pioneers light-based data processing and transfer in chips, aiming to enhance computing efficiency. Their chips, utilizing photons and electrons, target AI operations and data transfer between chips. With a recent $300 million funding, Lightmatter collaborates with major tech companies to reduce energy consumption in AI and data centers.
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New tools are available to help reduce the energy that AI models devour
The MIT Lincoln Laboratory Supercomputing Center (LLSC) is developing techniques to reduce energy use in data centers, particularly for AI models. They have found that power-capping hardware and early stopping during AI training can significantly decrease energy consumption without impacting model performance. The LLSC has also created a software that allows data center owners to set power limits on GPUs. They have developed a model for hyperparameter optimization that reduces energy waste, and an optimizer that matches models with the most carbon-efficient mix of hardware for inference. These interventions have the potential to advance the way AI models are trained and decrease energy consumption by 10-20%. The LLSC team is promoting transparency and sustainability in the industry and is collaborating with manufacturers and the U.S. Air Force to implement their energy-saving techniques in other data centers.