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Item type:Publication, An air quality monitoring and forecasting system for Lima city with low-cost sensors and artificial intelligence models(Frontiers Media S.A., 2022-07-07)Monitoring air quality is very important in urban areas to alert the citizens about the risks posed by the air they breathe. However, implementing conventional monitoring networks may be unfeasible in developing countries due to its high costs. In addition, it is important for the citizen to have current and future air information in the place where he is, to avoid overexposure. In the present work, we describe a low-cost solution deployed in Lima city that is composed of low-cost IoT stations, Artificial Intelligence models, and a web application that can deliver predicted air quality information in a graphical way (pollution maps). In a series of experiments, we assessed the quality of the temporal and spatial prediction. The error levels were satisfactory when compared to reference methods. Our proposal is a cost-effective solution that can help identify high-risk areas of exposure to airborne pollutants and can be replicated in places where there are no resources to implement reference networks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reviewing the influence of sociocultural, environmental and economic variables to forecast municipal solid waste (MSW) generation(Elsevier B.V., 2022-09-01)Municipal solid waste (MSW) generation forecasting has become an important tool for decision-making in urban environments, not only due to its essential role in effective waste management, but also because it provides an understanding of the complexity of the factors that govern it. Current research bases its forecast models (e.g., artificial neural networks, regression methods, three decision methods…) on predictive variables supported by pre-existing government information or, alternatively, on related studies with different site characteristics due to the lack of primary data from the specific sector. These assumptions and generalizations generate a different representation of the area of interest, raising the level of uncertainty of the results and reducing their level of reliability. The current review focuses on exploring the influence, relevance and opportunities for improvement when it comes to including or excluding sociocultural, environmental and/or economic variables in the solid waste forecasting process. Relevant information has been provided regarding the predictor variables considered to have better predictive power and, at the same time, limitations in data availability have been highlighted. Finally, it is concluded that the adoption of case study-specific predictor variables collected through primary data (e.g., questionnaires or surveys) would improve the predictive performance of the models providing a robust and effective tool for waste management. In addition, it is expected that the recommendations provided will be useful for future research related to MSW prediction and, thus, contribute to obtaining more representative results. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transportation energy demand forecasting in Taiwan based on metaheuristic algorithms(Taylor and Francis Ltd., 2022-01-01)A new methodology is suggested in this study to provide optimum forecasting of the future transportation energy demand in Taiwan. The paper introduces a new improved version of Emperor Penguin Optimizer (IEPO) to provide an optimal and suitable forecasting model. The forecasting was based on three different models including linear, exponential, and quadratic where their coefficients have been optimized using the suggested IEPO algorithm which is based on considering the population, the GDP growth rate, and the total annual vehicle-km. The study considers two different scenarios based on curve fitting and projection data. The results indicate that the RMS value for the TED forecasting based on the proposed IEPO algorithm applied to the linear, exponential, and Quadratic for training are 0.0452, 0.0461, and 0.0492, respectively and for testing are 0.0456, 0.0596, and 0.0642, respectively. This shows better results of the optimized exponential method’s efficiency. Simulation results showed high efficiency for the proposed IEPO-based transportation energy demand forecasting based on all of the employed models for decision-making in ROC. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Impact of measured spectrum variation on solar photovoltaic efficiencies worldwide(Elsevier Ltd, 2022-08-01)In photovoltaic power ratings, a single solar spectrum, AM1.5, is the de facto standard for record laboratory efficiencies, commercial module specifications, and performance ratios of solar power plants. More detailed energy analysis that accounts for local spectral irradiance, along with temperature and broadband irradiance, reduces forecast errors to expand the grid utility of solar energy. Here, ground-level measurements of spectral irradiance collected worldwide have been pooled to provide a sampling of geographic, seasonal, and diurnal variation. Applied to nine solar cell types, the resulting divergence in solar cell efficiencies illustrates that a single spectrum is insufficient for comparisons of cells with different spectral responses. Cells with two or more junctions tend to have efficiencies below that under the standard spectrum. Silicon exhibits the least spectral sensitivity: relative weekly site variation ranges from 1% in Lima, Peru to 14% in Edmonton, Canada. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forecasting Peru’s GDP Growth and Inflation Using TVP-VARMA-SV Models(Pontificia Universidad Católica del Perú. Departamento de Economía, 2026-08)This paper evaluates the forecasting performance of the time-varying parameter VARMA model with stochastic volatility (TVP-VARMA-SV) of Chan and Eisenstat (2017) for Peru’s GDP growth and inflation over 1994Q1–2019Q4. Seven model specifications are compared using density and point forecast evaluation metrics. The main results show that models with a movingaverage (MA) component generally deliver better forecast performance. Log predictive likelihoods (LPLs) indicate that models with MA, stochastic volatility (SV), or both components provide the best density forecasts at the one-quarter horizon, while simpler VARMA models perform better at the four-quarter horizon. Mean squared forecast errors (MSFEs) and Theil’s U are also lowest for models with an MA component. Probability integral transformation (PIT) histograms show that models with MA and SV components achieve the best calibration at the one-quarter horizon, while raw-moments tests indicate that the TVP-VARMA-SV, VARMA, VAR, and Bayesian model selection (BMS) specifications perform best at the four-quarter horizon. The model confidence set (MCS) frequently includes simpler models, particularly those with an MA term, and Diebold-Mariano tests also tend to favor models with an MA component. Overall, the BMS strategy improves forecast accuracy by dynamically selecting the best-performing models. An extension to exchange rate growth forecasts shows that MA-based models again perform best, with the TVP-VARMA-SV specification delivering the largest gains at the four-quarter horizon. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling ionograms and critical plasma frequencies with neural networks(Frontiers Media, 2025-01-01)Ionosondes offer broad spatial coverage of the lower ionosphere, supported by a global network of affordable instruments. This motivates the exploration of new methods that exploit this geographical coverage to capture spatially dependent characteristics of electron density distributions using data-driven models. These models must have the versatility to learn from ionogram data. In this work, we used neural networks (NN) to forecast ionograms across two solar activity cycles. The ionosonde data was obtained from the digisonde at the Jicamarca Radio Observatory (JRO). Each NN comprises one NN that estimates the ionogram trace and another one that estimates the critical frequency. Two forecasting models were implemented. The first one was trained with all available data and was optimized for accurate predictions along that time range. The second one was trained using a rolling-window strategy with just 3 months of data to make short-term ionogram predictions. Our results show that both models are comparable and can often outperform predictions by empirical and numerical models. The hyperparameters of both models were optimized using a specialized library. Our results suggest that a few months of data was enough to produce predictions of comparable accuracy to the reference models. We argue that this high accuracy is obtained with short time series because the NN captures the dominant periodic drivers. Finally, we provide suggestions for improving this model.1