The research, conducted by Ajay D. Sarange of Swami Ramanand Teerth Marathwada University and Kishor Y. Ingale of Netaji Subhashchandra Bose Arts, Commerce, and Science College in Nanded, India, is notable less for any single technique than for the disciplined way it connects every stage of a data-to-decision pipeline. Seventeen city-zone load series from Laayoune, Boujdour, Foum El Oued, and Marrakech were converted to a common unit, aggregated into four city-level series, and aligned on shared thirty-minute timestamps spanning a full year, from January 2023 to January 2024. The result is an experimental aggregate demand portfolio of nearly half a million observations that the authors stress is methodological rather than physical: the cities are not electrically interconnected, and no claim is made that a single retailer serves them all.

The forecasting stage is deliberately conservative. Three candidate models, a persistence forecast, a seasonal-naive forecast lagged by one day, and a Random Forest, were compared using only chronological training and validation splits, with no shuffling that could leak future information. The Random Forest, fed nothing but the previous twelve hours of half-hourly loads, won selection on validation error and was then asked to produce recursive 96-period forecasts, equivalent to 48-hour horizons, in which its own predictions feed back into subsequent steps. Crucially, the held-out test data were never touched during model choice or scenario construction. Interestingly, the seasonal-naive method later achieved lower test-period error than the selected Random Forest, an outcome the authors report candidly; the scheduling contribution, they emphasise, is not forecasting superiority but the disciplined treatment of uncertainty.

That uncertainty enters through a moving-block bootstrap applied to validation-period forecast residuals. Rather than resampling isolated errors, which would destroy the short-term correlation that matters for scheduling, the method draws contiguous blocks of residuals, 51 half-hours long in this configuration, and adds them to the point forecast to generate 10,000 plausible demand trajectories. A KMeans clustering procedure then compresses this vast pool into one hundred representative scenarios, each an actual generated trajectory selected as nearest to a cluster centroid rather than an averaged profile that would artificially smooth peaks. The authors deliberately compared averaged centroids against these representative selections, finding that representatives better preserved variance and extreme peaks, a detail that matters enormously when the whole point is to prepare for demand spikes.

The decision model itself is a two-stage stochastic programme, a classic operations-research structure with a modern twist. Before demand is known, the scheduler commits to day-ahead procurement, demand-response capacity, and reserve capacity. After a demand scenario materialises, recourse actions kick in: dispatching the committed demand response, deploying reserves, absorbing modelled unmet demand as a penalised slack, or dumping surplus. The mathematics enforces non-anticipativity, meaning first-stage decisions cannot secretly depend on which scenario later occurs. Three variants were compared under identical operational assumptions: a risk-neutral stochastic programme, a Conditional Value-at-Risk formulation that penalises the worst five percent of cost outcomes, and a study-specific formulation dubbed PMAD-SP, which adds a penalty on the maximum periodwise absolute deviation of scenario costs from their mean.

The headline result is striking. Evaluated on a separately generated common set of 10,000 scenarios that played no role in building any policy, the stochastic programme reduced expected normalised cost by 12.72 percent relative to the deterministic benchmark, and slashed the model’s penalised unmet-demand quantity by 87.42 percent. The mechanism is intuitive: the deterministic plan, anchored to one forecast path, committed no reserve capacity at all, leaving it dangerously exposed whenever demand exceeded the forecast. The stochastic plan paid slightly more for day-ahead procurement and for reserves, roughly 71 kilowatts of expected reserve commitment versus zero, but that insurance premium paid for itself many times over when reality diverged from the point forecast.

The risk-aware variants tell more nuanced stories. The CVaR formulation added about 18 percent more reserve commitment than the risk-neutral model and nudged empirical upper-tail cost down by just 0.54 percent, a modest gain the authors attribute to the finite resolution of the hundred-scenario planning tail. PMAD-SP went much further on insurance, boosting reserve commitment by 137 percent, and achieved its real objective: it cut the standard deviation of scenario-total costs by nearly 40 percent while leaving expected cost essentially unchanged. The authors are careful to position PMAD-SP as descriptive cost-dispersion regularisation rather than formal distributionally robust optimisation, since it optimises against a fixed empirical distribution rather than an ambiguity set of possible distributions.

Sensitivity analyses probe the framework’s robustness. Because three of the four cities record current in amperes rather than power, the conversion to kilowatts required assumed values for voltage and power factor; testing nine combinations across 220 to 240 volts and power factors of 0.80 to 1.00 showed the stochastic programme’s advantage persisting in every case, with cost reductions ranging from 12.57 to 12.89 percent. Seven consecutive non-overlapping 48-hour historical windows also saw the stochastic policy win every time, a perfect record confirmed by an exact sign test, though the authors insist this evidence is descriptive because the windows share fitted models, calibration residuals, and adjacent chronology. Formal decision-value calculations reinforce the picture: the value of the stochastic solution amounts to 10.44 percent of the recourse problem’s optimal cost, while perfect foresight would be worth a further 3.67 percent.

The study is equally forthright about its limits. The single-node portfolio carries no network constraints, power flows, storage, renewables, or weather covariates; the cost coefficients are normalised experimental values rather than Moroccan tariffs; and the unmet-demand variable is a penalty-sensitive optimisation slack, not a measure of real blackouts. Reducing the hundred representative scenarios still discards nearly 48 percent of raw periodwise variance, and the risk weights were manually chosen rather than calibrated. Yet the core lesson survives these caveats intact and carries real weight for the energy transition. As smart meters proliferate across Africa, Europe, and beyond, the gap between forecasting demand and scheduling under demand uncertainty is where money is won or lost. This work shows, with unusual methodological transparency, that treating uncertainty as a first-class citizen of the optimisation problem, rather than an afterthought appended to a forecast, changes what a rational retailer buys, reserves, and dispatches, and by amounts large enough to matter.

Subject of Research: Scenario-based stochastic programming for electricity demand scheduling using smart-meter data

Article Title: Scenario based stochastic programming for aggregate electricity demand scheduling using Moroccan smart meter data

Article References: Sarange, A. D., & Ingale, K. Y. (2026). Scenario based stochastic programming for aggregate electricity demand scheduling using Moroccan smart meter data. Discover Informatics, 1(1), Article 19. https://doi.org/10.1007/s44564-026-00021-2

Image Credits: AI Generated

DOI: 10.1007/s44564-026-00021-2

Keywords: stochastic programming, smart meters, load forecasting, electricity demand scheduling, scenario generation, Conditional Value-at-Risk, Random Forest, Morocco, energy management, two-stage optimization, demand response, bootstrap

The work, led by Nastaran Enshaei of Concordia University’s Institute for Information Systems Engineering together with Patrick Paul and Stéphane Tremblay of the National Research Council Canada, and corresponding author Ashkan Ebadi, tackles a deceptively simple question: can a camera and a neural network do the job of an expensive meter upgrade? The answer, according to the team, is yes — and with a level of precision that surprises even energy analysts. DeepGATE reads the pointer movements of mechanical dials from real-time images and resolves gas consumption to within 0.001 cubic meters, a thousandfold improvement in resolution over the 1-cubic-meter granularity of a typical dial-based manual read.

Why does that resolution jump matter? Mechanical gas meters accumulate consumption continuously, but the least significant dials creep along slowly, and a human reader or a coarse automated system may only register changes of a cubic meter or more between readings. At that resolution, a household’s short bursts of consumption — a shower, a stove burner igniting, a furnace cycling on a cold morning — simply vanish between snapshots. By reading the fine-grained pointer positions directly, DeepGATE captures those small events, which is exactly the granularity needed for occupancy behavior monitoring, consumption pattern analysis, and personalized efficiency guidance for homeowners.

The technical pipeline behind the framework blends classic computer vision with modern deep learning. The system first processes images of the meter face, where multiple circular dials carry pointers whose angular positions encode digits of cumulative consumption. The researchers devised both conventional and dataset-specific data augmentation strategies to cope with the diverse artifacts that plague outdoor imaging — glare, frost, rain, shadows, and the variable lighting of Canadian weather, since the training imagery comes from real meters mounted outside homes. These augmentation techniques expand the effective diversity of training data, allowing the network to remain robust when real-world conditions diverge from the idealized images it was trained on.

Under the hood, the researchers drew on a lineage of object detection and recognition architectures that the field has refined over the past decade — from Faster R-CNN and SSD through the YOLO family — and on proven backbone networks such as ResNet, VGG, and DenseNet for feature extraction. Crucially, they prioritized a lightweight design. Rather than chasing maximum accuracy with a massive model, the team engineered DeepGATE to run on edge devices: small, low-power computers that can be attached near the meter itself. That means no video has to stream to a cloud server, readings are computed locally and instantly, and the entire retrofit cost amounts to a camera, a compute module, and a power connection rather than a full meter replacement and the utility truck rolls that go with it.

The problem DeepGATE addresses is bigger than convenience. Smart meters and advanced metering infrastructure have documented benefits — leakage detection, demand forecasting, dynamic billing — but upgrading every mechanical meter carries significant cost, and studies of advanced metering infrastructure have flagged technology, security, and governance challenges as well. Meanwhile, accurate consumption data has become an urgent climate tool. Natural gas is positioned as an essential bridge fuel for residential heating in the early stages of the low-carbon transition, and precise monitoring lets utilities forecast demand more accurately, lets regulators understand usage patterns, and lets consumers see exactly how their daily habits translate into cubic meters of fuel burned.

The research also extends a body of computer vision work on automatic meter reading that stretches back more than a decade. Earlier efforts tackled gas meter reading from real-world images with multi-network systems and angle-invariant methods, and more recent approaches have applied convolutional neural networks to water meters, electricity meters, pointer gauges in substations and natural gas stations, and SF6 pressure gauges. Each of those systems fought the same enemy: unconstrained real-world conditions. What distinguishes the new work is its combination of fine pointer-angle precision, explicit handling of weather-induced image degradation, edge-device deployability, and a training resource built for the task. The team built on their own NRC-GAMMA dataset, a large-scale collection of gas meter images that they have made publicly available to the research community via GitHub — an unusually open move in a field where proprietary data is the norm.

Interpretability played a role in the design as well. The study leverages gradient-based localization techniques such as Grad-CAM, which let researchers visualize which regions of an image the network attends to when it makes its reading. That kind of visibility matters in a monitoring application: if a network is going to translate a blurry, frost-covered dial into a billing-relevant number, both engineers and eventual users need confidence that the model is looking at the pointer and not at a shadow or a scratch on the glass. The framework’s cross-validation-driven evaluation, guided by established statistical practice, reinforces that confidence by testing generalization rather than memorization.

The authors are explicit about the framework’s generality. DeepGATE is adaptable to the automated reading of diverse non-smart energy meters — water, electricity, and industrial gauges among them — and the augmentation strategies devised for weather-related artifacts transfer readily to other deep learning applications in outdoor image processing. In effect, the contribution is twofold: a working system for gas consumption monitoring, and a set of reusable techniques for any computer vision task where cameras must survive the elements. The dataset release alone could accelerate research, since robust analog gauge reading has long been hampered by a scarcity of labeled, real-world imagery.

The downstream implications reach into behavior science and energy policy. The researchers point to improved occupant behavior monitoring systems as a key application: with 0.001-cubic-meter resolution, a household’s consumption fingerprint becomes rich enough to distinguish cooking from heating from hot-water use, enabling customized consumption guidance that could nudge households toward measurable efficiency gains. For utilities, real-time edge inference means consumption data without privacy-eroding cloud pipelines, and for the low-carbon transition it means that the installed base of dumb meters — millions of devices with decades of mechanical life left in them — can be drafted into the smart grid revolution rather than scrapped. As deep learning continues its march into infrastructure, DeepGATE offers a quietly compelling vision: sometimes the smartest way to upgrade the grid is to teach a small computer to do what a human reader does, only a thousand times more precisely, every moment of every day.

Subject of Research: Deep learning-based automatic reading of non-smart gas meters for real-time residential energy consumption monitoring

Article Title: DeepGATE: a deep learning-based automatic meter reading framework for real-time gas consumption monitoring

Article References: Enshaei, N., Paul, P., Tremblay, S., & Ebadi, A. (2026). DeepGATE: a deep learning-based automatic meter reading framework for real-time gas consumption monitoring. International Journal of Data Science and Analytics, 22(1), Article 309. https://doi.org/10.1007/s41060-026-01273-9

Image Credits: AI Generated

DOI: 10.1007/s41060-026-01273-9

Keywords: deep learning, automatic meter reading, gas consumption monitoring, computer vision, non-smart gas meters, edge computing, data augmentation, energy analytics, smart meters, NRC-GAMMA dataset, neural networks, energy informatics

The research, led by Milad Rajaei with Usman Safder, Sarah Cotterill, and Recep Kaan Dereli, presents a city-scale framework that simulates water demand in near real time by explicitly tracking how people move through a city. The work builds on SIMDEUM, a well-established stochastic end-use model originally developed in the Netherlands, which represents water consumption as a stream of random pulses—each toilet flush, shower, tap use, or dishwasher cycle—with timing, duration, and flow rate drawn from probability distributions derived from empirical observations of occupant behaviour. SIMDEUM has proven remarkably capable of reproducing realistic household demand patterns at high temporal resolution, and it has been extended to offices, hotels, nursing homes, and other non-residential buildings by dividing each building into functional rooms with their own appliances and users. But the model has always carried a fundamental weakness: it assumes occupancy is either static or averaged, which in dynamic urban environments can become the dominant source of error.

The importance of occupancy is not in doubt. Sensitivity analyses of stochastic residential demand models have found Spearman’s rank correlation coefficients between occupancy and both peak and average demand ranging from 0.99 to 0.995—an almost perfect relationship. Field experiments reinforce the point: researchers who installed flush counters on 119 toilets across seven university campus buildings demonstrated a strong, direct link between toilet water use and the number of people present. The COVID-19 pandemic made the consequences of ignoring this relationship vividly clear, as commuting collapsed, workplaces emptied, and hygiene practices intensified, producing higher residential demand alongside sharply reduced commercial consumption. Models grounded in static occupancy assumptions simply could not see these shifts coming.

The researchers’ insight was to recognise that the data needed to model dynamic occupancy already exists in aggregate form. Google Popular Times indicators describe how busy non-residential locations are at any given moment, derived from aggregated and anonymised smartphone location data, expressed on a relative scale from 0 to 100 compared with a location’s typical peak activity. The framework begins by collecting these signals at five-minute intervals through automated web scraping for every non-residential building in a study area. Where live data are available and pass quality checks, they are used directly; where they are not, the model descends through a careful hierarchy of fallbacks—historical average patterns for the same day of the week, then representative occupancy profiles derived from K-means clustering of tens of thousands of profiles collected nationwide, then literature-based profiles from U.S. Department of Energy reference buildings, adjusted with local correction factors for seasonal effects such as school terms and hotel occupancy statistics.

Converting relative busyness into absolute occupant numbers requires a further step: each Popular Times value is multiplied by the estimated capacity of the building, calculated by dividing floor area by occupancy load factors taken from building design and fire safety guidelines, and scaled to reflect normal operation rather than maximum permitted crowding. Data quality proved to be a genuine challenge. Across a one-month collection period in September 2024, 58 percent of live samples were classified as invalid under the study’s quality-control rules, which flagged suspicious sudden drops in occupancy that persisted briefly before abruptly returning to normal—patterns unlikely to represent real activity. Days with insufficient valid data were replaced wholesale with historical averages, while shorter gaps were filled by linear interpolation. The prevalence of anomalies, particularly at low-traffic locations, underscores that crowdsourced occupancy data is useful but demands rigorous preprocessing.

The most conceptually ambitious element of the framework is its treatment of residential occupancy, for which no direct crowdsourced signal exists. Rather than relying on census averages, the model infers where people are at home by tracking population movements. Drawing on two classic theories of human mobility—Zipf’s gravity model, which holds that movement likelihood rises with population and falls with distance, and Stouffer’s intervening opportunities model, which assumes people choose the nearest destination that satisfies their needs—the framework constructs a trip probability matrix at each time step. When non-residential occupancy rises, the corresponding number of people is drawn probabilistically from residential areas weighted by their populations and the distribution of nearby opportunities; when occupancy falls, people return to their original home areas. A tourist population, calibrated from national accommodation and tourism statistics, handles movements associated with hotels and nightlife, while a separate commuter population accounts for people travelling into the study area from outside.

These time-varying occupancy estimates then feed directly into a modified SIMDEUM model running at one-minute resolution in MATLAB. At each time step, the probability of a water-use event for each end-use is calculated from the occupancy, the per-person frequency of use, and a diurnal timing factor reflecting behavioural routines. A random draw is compared with this probability to decide whether an event occurs, and if so, its flow rate and duration determine the volume consumed, with each end-use temporarily locked during an event to prevent overlap. For occupancy-dependent end-uses such as toilet flushing and hand washing, the occupancy term drives the calculation; for scheduled activities such as office cleaning, occupancy is effectively set aside so that only frequency and timing matter.

Applied to Sligo, a coastal town of roughly 20,000 people in northwest Ireland, the framework simulated an entire month of city-scale demand. Residential consumption came out at approximately 129 litres per person per day—closely matching the metered benchmark of about 312 litres per household per day reported for Sligo—and the simulated end-use breakdown, with toilets accounting for 28 percent of consumption, showers 24 percent, and kitchen taps 21 percent, differed by no more than two percentage points from published values for Irish households. The temporal patterns behaved as one would expect: a pronounced morning peak between 6:00 and 9:00 a.m. on weekdays driven by showering and breakfast routines, a delayed peak on weekends, a midday dip as residents left for work or school, and an evening recovery as people returned home. Non-residential demand told equally coherent stories—restaurants showed sharp peaks aligned with mealtimes, food retail displayed the steadier profile of continuous cleaning and toilet use, and office buildings peaked at the start of the working day before collapsing after closure.

The study is candid about its limitations. The default SIMDEUM parameters derive from Dutch household data and may not transfer cleanly to Irish conditions—simulated restaurant water use of 6.2 litres per square metre per day diverged from the 2.48 litres reported by Irish Water for comparable commercial premises, a discrepancy the authors attribute primarily to uncalibrated appliance frequencies, durations, and flow rates. The conversion of relative busyness into absolute occupant counts also requires independent validation against footfall sensors or building occupancy systems. Nevertheless, the results demonstrate something genuinely significant: a scalable, transferable route to high-resolution water demand modelling that requires no dense smart metering infrastructure whatsoever. By resolving demand at the level of individual end-uses across entire cities, the framework opens the door to demand-responsive pumping schedules, energy-aware operation of distribution networks, and scenario testing for planners—capabilities that could meaningfully reduce the energy intensity and emissions of the urban water cycle, one flush at a time.

Subject of Research: A real-time, city-scale water demand modelling framework that integrates urban mobility data from Google Popular Times with a stochastic end-use water demand model.

Article Title: A framework for real-time water demand modelling at city scale based on urban mobility

Article References: Rajaei, M., Safder, U., Cotterill, S., & Dereli, R. K. (2026). A framework for real-time water demand modelling at city scale based on urban mobility. Energy Reports, 16, Article 109700. https://doi.org/10.1016/j.egyr.2026.109700

Image Credits: AI Generated

DOI: 10.1016/j.egyr.2026.109700

Keywords: water demand modelling, urban mobility, Google Popular Times, SIMDEUM, smart meters, occupancy, water-energy nexus, end-use model, stochastic simulation, urban water systems, Sligo Ireland, energy efficiency