ArticleHeliyon2025
Smart homes energy management: Optimal multi-objective appliance scheduling model considering electrical energy storage and renewable energy resources.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer.Biomimetics (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As smart homes (SHs) integrate into distribution systems, microgrid scheduling has become increasingly important because of their schedulable loads that reduce peak loads. Accordingly, a multi-objective optimization approach is presented for SH energy management (SHEM) and demand response (DR) programs with 30-min time slots. Time-of-use tariffs are used in the suggested scheme, and the primary goal is to minimize the daily bills and peak-to-average ratio (PAR), simultaneously. This scheme includes flexible and fixed home appliances. Here, the SHEM system consists of photovoltaic and wind turbine systems in combination with an electrical energy storage (EES) system to provide optimum peak load performance at peak times, based on the discharging and charging mechanism. Also, in the proposed mathematical formulation, the bought and selling energy is considered during the day. An improved Biogeography-based optimization algorithm (IBBO) is used to solve the multi-objective problem. The first step is to develop the equations for general electrical appliances of particular SH consumers, and then minimize the mentioned two objectives. Based on the outcomes under different scenarios such as different sizes of renewable energy resources, various charging/discharging rates, and different selling electricity tariff ratios, PAR and operational costs are reduced, and the electricity is sold to upstream. Moreover, simulations show that the suggested scheme produces the optimal outcomes, in which both objectives are near their optimal levels, as shown in the Pareto Front of the optimal solutions. The maximum standard deviation of total objective function between all cases for IBBO, gray wolf optimizer (GWO), and whale optimization algorithm (WOA) are 6.55, 17.22, and 24.87, respectively, which show the robustness of IBBO in finding the best solution in comparisons of other algorithms. Also, the average solution of IBBO is lower than GWO, and WOA, which shows the performance and superiority of IBBO in finding the best solution.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.