Artificial Intelligence Energy Industry (67 results)

Condition: New.

Hardback or Cased Book. Condition: New. Artificial Intelligence in the Energy Industry. Book.

Condition: As New. Unread book in perfect condition.

Condition: New.

Hardback. Condition: New.

Condition: New.

Condition: New. 2025. paperback. . . . . .

Condition: New. In English.

Taschenbuch. Condition: Neu. Neuware - Artificial Intelligence in Industry: A Professional Guide to How Artificial Intelligence Is Transforming Manufacturing, Energy, and OperationsEvery industrial organization is asking the same question: where do we actually start with AI Not the marketing version - the real one, asked by engineers who need to trust a model before they act on it, technicians navigating a new tool on the floor, and executives who need a business case that still holds up eighteen months later.This book answers that question honestly. Starting with plain-language foundations - what AI, machine learning, and deep learning actually are, and how a model learns - it moves through Industry 4.0, 5.0, digital twins, and the Industrial Internet of Things before diving into where AI delivers real, documented value: predictive maintenance, quality inspection, and safety monitoring, the three best-proven use cases in industrial AI today.Nine industries get dedicated treatment, from manufacturing and oil and gas to semiconductor fabrication and pharmaceutical production. Five chapters speak directly to engineers, technicians, operators, managers, and decision-makers. A worked financial example, a one-page project charter, a vendor RFP question bank, and a readiness checklist turn concepts into action.What sets this book apart: it doesn't oversell. Alongside every success story sits the honest statistic - most AI pilots never scale, and this book explains why, and how to be the exception. Fifty genuine, linked sources back every claim.For engineers, technicians, managers, and decision-makers who want a realistic, well-sourced understanding of what AI can - and can't - do for the operations that keep the physical world running.…

Condition: As New. Unread book in perfect condition.

Condition: New. 2025. paperback. . . . . . Books ship from the US and Ireland.

Condition: New. Print on Demand.

Condition: New.

Condition: New. 1st edition NO-PA16APR2015-KAP.

Condition: As New. Unread book in perfect condition.

Condition: New.

Hardback. Condition: New.

Condition: New.

Condition: New.

Condition: new.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. In English.

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Energy-intensive sectors such as industry and transportation are vital to economic development, yet remain major contributors to global energy consumption and greenhouse gas emissions. Enhancing their energy efficiency is essential to achieving climate resilience and meeting decarbonization goals. Despite abundant operational and sensor data, decision-making in these sectors often relies on heuristics and lacks systematic, transparent, and explainable analyticalsupport. Existing Artificial Intelligence (AI)-based decision support methods frequently fall short in integrating robust data preprocessing, causal interpretation, and actionable recommendation generation, limiting their practical impact.This book develops and validates an AI-driven decision support framework to improve energy efficiency in energy-intensive industrial and transportation systems. Guided by a three-cycle Design Science Research (DSR) methodology, the framework integrates complementary AI techniques into a modular, end-to-end architecture that transforms heterogeneous raw data into interpretable, actionable recommendations. It includes systematic data quality assessment and preprocessing pipelines, time-series segmentation, clustering key performance indicators for pattern recognition, causal inference methods to identify drivers of inefficiency, and a multimodal large language model (LLM)-based decision support module that translates analytical outcomes into domain-relevant strategies.…

Paperback. Condition: Brand New. 407 pages. 5.83x0.91x8.27 inches. In Stock.

Condition: New.

Hardcover. Condition: Brand New. 400 pages. 9.02x5.98x1.14 inches. In Stock.

Condition: New.