AI in the Home: New Review Maps the Future of Smart Energy and Water Savings

Imagine a house that knows when your water tank is running low, shifts your washing machine to the moment your rooftop solar panels are producing the most electricity, and quietly flags a leaking pipe before the damage spreads. That vision is moving closer to reality, but according to a new systematic review, the science behind it remains surprisingly fragmented. Researchers at the University of Nigeria, Nsukka, led by Chisom E. Ogbuanya, have published one of the most comprehensive assessments to date of artificial intelligence applied to smart home resource management, and their verdict is double-edged: the technology works, sometimes spectacularly, but almost nobody is optimizing energy and water together. The review, published in Frontiers in Control Engineering, follows the PRISMA 2020 guidelines and distills a fast-moving field into five interconnected themes: home energy management, non-intrusive load monitoring, reinforcement learning control, smart water management, and the security and privacy of the Internet of Things.

The team searched Google Scholar and IEEE Xplore for peer-reviewed studies published between 2021 and 2025, supplementing the search with manually incorporated papers indexed in Scopus and Web of Science and updated through early 2026. From an initial pool of 92 records, the researchers removed 14 duplicates, screened 78 titles and abstracts, excluded 30, retrieved 48 full texts, and discarded 16 more, ultimately including 32 studies in the final synthesis. Two reviewers performed screening and data extraction independently, resolving disagreements by consensus and consulting a third reviewer when needed. Because the included studies varied wildly in design, spanning experimental prototypes, simulations, conceptual frameworks, and prior reviews, the authors judged that a conventional statistical meta-analysis was impractical. Instead they employed a narrative, thematic synthesis, assessing each study’s methodological transparency, the adequacy of its evaluation metrics, the validity of its results, and its honesty about limitations.

The single largest body of work concerns smart home energy management systems, known as HEMS, which combine IoT sensors, consumption modeling, and optimization algorithms to schedule appliances, shave peak demand, and interact intelligently with the electricity grid. The reported numbers are striking. Studies in the review documented energy savings ranging from 15 to 25 percent under machine learning and rule-based control, while fuzzy logic controllers achieved savings of up to 70 percent under dynamic and uncertain load conditions. Artificial neural networks improved predictions of household consumption enough to refine lighting and HVAC control, and deep learning models, particularly long short-term memory networks, boosted load forecasting accuracy and demand response performance. Cloud-connected dashboards fed by smart meters, current transformers, and voltage sensors gave households real-time visibility into their consumption, sometimes through nothing more exotic than a messaging app, enabling remote appliance control and behavioral feedback.

Beneath these headline savings lies a more granular technique that the review treats as a field in its own right: non-intrusive load monitoring, or NILM. Rather than instrumenting every appliance with its own smart plug, NILM infers what individual devices are doing by analyzing the aggregate electrical signature measured at a single point, typically the main meter. Machine learning models extract power signatures and consumption patterns from waveform data, disaggregating the household total into appliance-level activity. This enables targeted interventions, such as identifying an inefficient refrigerator or revealing wasteful usage habits, without additional hardware. One AIoT-based system in the reviewed literature reported 35 percent energy savings alongside anomaly detection. Yet the authors found dedicated NILM research scarce, with the technique usually embedded invisibly inside broader monitoring platforms, and they flagged a particularly acute obstacle: the absence of large annotated datasets from African households, which limits the development and validation of models for the settings where such technology could matter most.

Reinforcement learning represents the most ambitious corner of the field. Here, a control agent learns by trial and feedback, continuously adjusting its decisions to balance comfort, cost, and efficiency under uncertainty. The reviewed studies demonstrated adaptive appliance scheduling, HVAC regulation that responds to shifting environmental variables, and dynamic load shifting that avoids expensive peak periods. One deep reinforcement learning system cut electricity costs by 31.5 percent while improving power factor, and a hybrid deep reinforcement learning framework paired with explainable AI improved water-energy efficiency in urban management. A separate study used proximal policy optimization, a state-of-the-art RL algorithm, to achieve adaptive appliance control. The catch, the authors emphasize, is that most of these successes live in simulation. Running learning agents on the resource-limited microcontrollers that populate real smart home prototypes remains difficult, and practical concerns such as safety during exploration, sample efficiency, and respect for ingrained user habits are rarely addressed in the literature.

Water, the review’s quieter protagonist, receives far less attention than electricity despite its growing scarcity. The smart water studies that made the cut relied heavily on IoT instrumentation: soil-moisture probes for irrigation, ultrasonic tank-level detectors, flow sensors, and leak detectors feeding real-time data to cloud dashboards and mobile interfaces. Some systems autonomously activated pumps when storage levels dropped, reducing both manual operation and pumping costs. More sophisticated approaches brought digital twins, virtual replicas of distribution networks, together with machine learning and reinforcement learning to optimize water treatment and predict consumption, while gamified feedback systems encouraged conservation behavior, particularly among younger residents. Tank-level sensing emerged as a low-cost, non-intrusive alternative to full smart metering, well suited to regions with intermittent supply. But the authors note a systemic blind spot: most water prototypes address a single function, and almost none consider the energy embedded in every liter pumped, heated, or treated.

That blind spot is the review’s central criticism. Households face coupled resource flows, where scheduling a water pump, a water heater, or a treatment load directly changes the electrical load profile, yet the overwhelming majority of research optimizes one resource in isolation. Multi-objective frameworks that jointly balance energy cost, water usage, occupant comfort, and equipment longevity remain underdeveloped. The authors also identify a gap between algorithms and reality: many models are trained on datasets from foreign households and may fail to generalize to homes with different occupancy patterns, climates, or appliance stocks. Economic evaluation is similarly thin, with studies reporting energy savings percentages but rarely installation costs, operating expenses, or payback periods. And while emerging directions such as edge AI, federated learning for privacy-preserving optimization, multi-agent reinforcement learning, transformer architectures, and hybrid swarm-intelligence optimizers promise more capable and adaptable systems, few have been tested in longitudinal, in-home deployments.

Security threads through every theme as a persistent brake on adoption. IoT smart homes capture intimate data streams, including motion patterns, appliance usage, and environmental conditions, and the reviewed studies repeatedly identified vulnerabilities in wireless channels, weak authentication, and unencrypted device-to-cloud transmission. Systems controlled through GSM networks or web dashboards are especially exposed to spoofing, interception, and unauthorized commands. Proposed countermeasures include graph convolutional networks for anomaly detection, deep learning-based security layers, device-level authentication, and encrypted communication, but the authors find that end-to-end security architectures, spanning secure transmission, access control, interoperability standards, and transparent user consent, are largely absent. Inexpensive control schemes built on platforms like WhatsApp demonstrate that smart home functionality can reach resource-limited settings, but they also illustrate how minimal the security layers in most prototypes truly are.

The review’s geographic framing gives it unusual urgency. Most studies originate in high-income countries with stable grids and mature digital infrastructure, while sub-Saharan Africa, where optimization could deliver the greatest impact, remains strikingly understudied. For Nigeria specifically, the authors see immediate openings: NILM could deliver appliance-level energy diagnostics without costly per-device metering; predictive and reinforcement learning controllers could synchronize appliance use with the country’s rapidly expanding rooftop solar capacity; and IoT tank monitoring and leak detection could reduce waste in a landscape dominated by boreholes and intermittent municipal supply. Their prescription is concrete: invest in local field deployments and multi-home datasets, build partnerships among universities, utilities, and technology firms, and design for resilience with edge computing, offline-capable systems, and low-cost hardware. The 32 studies synthesized here prove that AI can cut household energy use by double digits and watch over every drop of water. What remains is the harder engineering of integration, making one intelligent system manage both, affordably and securely, in the places that need it most.

Subject of Research: AI-based optimization of energy and water use in smart homes, based on a PRISMA systematic review of 32 studies from 2021 to 2025

Article Title: AI-driven smart home optimization for sustainable energy and water management: a systematic review

Article References: Ogbuanya, C. E., Eneh, J. N., & Udanor, C. N. (2026). AI-driven smart home optimization for sustainable energy and water management: a systematic review. Frontiers in Control Engineering, 7, Article 1793880. https://doi.org/10.3389/fcteg.2026.1793880

Image Credits: AI Generated

DOI: 10.3389/fcteg.2026.1793880

Keywords: smart homes, artificial intelligence, machine learning, reinforcement learning, home energy management systems, non-intrusive load monitoring, smart water management, Internet of Things, IoT security, systematic review, energy efficiency, Nigeria