An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

[EN] Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.This work was supported in part by the National Research Foundation of Korea Grant Funded by the Korea Government (MSIT) under Grant 2019M3F2A1073179; in part by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" Within the Project under Grant TIN2017-84802-C2-1-P; and in part by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET Joint Activities and Beyond) Project ERANETMED3-227 SMARTWATIR.Han, T.; Muhammad, K.; Hussain, T.; Lloret, J.; Baik, SW. (2021). An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks. IEEE Internet of Things. 8(5):3170-3179. https://doi.org/10.1109/JIOT.2020.3013306S317031798

Short-Term Load Demand Forecasting For Transnet Port Terminal (Tpt) In East London Using Artificial Neural Network

DissertationThe daily and weekly energy consumption patterns at the Transnet Port Terminal (TPT) in East London varies stochastically. This is as a result of the transient weather patterns that exist at the harbor. It has therefore become imperative to wisely manage this load in order to save electricity costs and for future infrastructure development. Hence the ongoing supply of electricity to port consumers requires an accurate and adequate short-term load forecast (STLF) for quality, quantity, and efficient management. Many researchers have recently proposed Artificial Neural Networks for short-term load prediction. However, most of the studies have not considered the quickly changing weather patterns that exist at the port. Therefore, the objective of this study is to establish a supervised short-term load prediction using ANN models, and to verify the effectiveness of such predictions by using the real load data from the TPT. The suggested system architecture uses open- loop training with real load and weather information, and then a closed-loop network is used to produce a prediction with the predicted load as its feedback data. Data collection points were set up in the ring network of the port by installing new power measuring meters, and weather data obtained from local meteorology offices in order to build a suitable alternative of localised data management (data base) for saving all data gathered. Hence, profiling of the load in the TPT was done and load forecasting was carried out, leading to improved load management strategies for the harbor terminal. ANN short-term load prediction (STLP) models were developed utilising its own performance to improve precision by essentially implementing a load feedback loop that is less reliant on external data. To ensure that the timeseries data recorded at the port were well modeled, the Nonlinear autoregressive exogenous model (NARX) for load prediction were developed using mean squared error (MSE) as a performance metric. Furthermore, to show the efficacy of the proposed model for STLP, the adaptive neuro-fuzzy inference system (ANFIS) was used with the same data for short-term predictions. The minimum mean squared errors obtained for both NARX and ANFIS models were 0.0010939 and 0.0032 respectively, indicating that the NARX model is more accurate during the forecast of departmental loads. The results of the predictions using the hourly timeseries indicated a close match between the forecasted and actual load demand at the port terminal. The effects of the load forecast could be used as a guide for implementing management plans for internal load, such as the generation of urgent electricity and the programme of implementation for demand-side management policies

Mejoras de eficiencia computacional y precisión para sistemas predictivos de demanda eléctrica

Programa de Doctorado en Tecnologías Industriales y de TelecomunicaciónDebido a la inviabilidad del almacenamiento de energía eléctrica a gran escala, la energía eléctrica se genera y consume simultáneamente. En consecuencia, las entidades eléctricas necesitan sistemas de previsión de demanda para planificar operaciones y gestionar suministros. Predicciones de demanda precisas permiten el ahorro económico de suministros de generación de energía, así como reforzar la fiabilidad del abastecimiento a los consumidores. Por otra parte, las predicciones de demanda también permiten gestionar las energías renovables en las redes eléctricas, reduciendo indirectamente las emisiones de gases de efecto invernadero. Esta tesis se centra en mejorar, a escala peninsular, el sistema predictivo de Red Eléctrica de España (REE) y desarrollado por la Universidad Miguel Hernández (UMH). Se presenta un enfoque independiente de sus modelos matemáticos, ofreciendo metodologías aplicables a otros sistemas predictivos de otras redes eléctricas. Se exploran dos mejoras: una obtención determinista y automática del horario de cálculos y un preprocesado de datos de temperatura que da pie a análisis demográficos. Ambas mejoras también incrementan la precisión de las predicciones, siendo un criterio base de diseño. En Europa, debido a las directivas y las nuevas tecnologías, los sistemas de predicción pasan de trabajar en intervalos horarios a cuarto-horarios, lo que reduce el tiempo de cálculo y aumenta la carga computacional. Por lo tanto, un sistema predictivo puede no disponer de tiempo suficiente para calcular todos los pronósticos futuros. Los sistemas de predicción realizan cálculos a lo largo del día, repitiendo los mismos pronósticos a medida que se acerca la hora prevista. Sin embargo, hay predicciones que no son más precisas que otras ya calculadas, lo que da pie a no ejecutarlas y emplear las predicciones previas para ahorrar esfuerzo computacional y mantener la precisión. Con la idea de evitar cálculos contraproducentes, se desarrolla un algoritmo que estima qué pronósticos brindan mayor precisión que los anteriores, con lo que construye un horario de ejecuciones. El algoritmo se adapta a las necesidades computacionales y el sistema predictivo, con lo que se ha aplicado al sistema de predicción de REE, obteniendo un horario de ejecuciones que consigue una mayor precisión y se adapta a la carga computacional. Por otra parte, la demanda eléctrica depende de la temperatura ambiente por el uso de equipos de aire acondicionado y calefacción. Esta tesis propone un método automático de procesamiento y selección de variables térmicas con un doble objetivo: mejorar tanto la precisión como la interpretabilidad del sistema de pronóstico global. La metodología experimental se ha realizado con el sistema predictivo de REE. La nueva forma de trabajar con las temperaturas es interpretable, ya que separan el efecto de la temperatura según la ubicación y el tiempo mediante variables con un significado específico. Ambos estudios demuestran experimentalmente que las técnicas propuestas cumplen su cometido, mejorando la precisión y el coste computacional del sistema predictivo. También se observa que en España el calor tiene mayor influencia sobre la demanda que el frío. En los días calurosos, la temperatura del segundo día anterior tiene mayor influencia que la del anterior, mientras que en los días fríos ocurre lo contrario. A partir de la construcción del horario de ejecuciones se ha concluido que las temperaturas afectan poco a la demanda durante la madrugada; las previsiones de temperatura de menos de cuatro días de antelación implican una mayor precisión que las de más de cuatro; y que cuanto menor es la diferencia de tiempo entre el momento de predicción y el de ejecución, mayor precisión se tiene.Due to the infeasibility of large-scale electrical energy storage, electrical energy is generated and consumed simultaneously. Therefore, electricity entities need demand forecasting systems to plan operations and to manage supplies. Improving the forecasts accuracy allows economic savings of energy generation supplies, as well as reinforcing the reliability of energy supply to electricity consumers. In addition, demand forecasts allow renewable energies to be managed in electricity networks, indirectly reducing greenhouse gas emissions. This thesis focuses on improving, at peninsular scale, the forecasting system of Red Eléctrica de España (REE) developed by the Miguel Hernández University (UMH). An independent approach of mathematical models is presented, offering methodologies applicable to other forecasting systems from different electrical grids. Two improvements are tackled: a deterministic and automatic schedule obtention and a preprocessing of temperature data, which can be used as a tool for demographic studies. Both enhancements also increase the forecasting accuracy. In Europe, due to directives and new technologies, forecasting systems are transitioning from hourly intervals to quarter-hourly intervals, which reduces the calculation time and increases the computational burden. Therefore, a predictive system may not have enough time to compute all future forecasts. Forecasting systems perform calculations throughout the day, repeating the same forecasts while the forecast time approaches. However, there are predictions that are not more accurate than others already calculated, which leads to not executing them and using previous predictions to save computational effort and maintain accuracy. With the intention of avoiding counterproductive calculations, an algorithm is developed, that estimates which forecasts provide better accuracy than previous ones, then it builds a computing schedule. The algorithm adapts to the computational needs and the predictive system. It has been applied to the REE prediction system, obtaining a computing schedule that achieves greater precision and adapts to the computational load. Temperature affects electricity consumption through air conditioning and heating equipment. This thesis proposes an automatic method of processing and selecting variables with a double objective: to improve both the accuracy and the interpretability of the global forecasting system. The experimental methodology has been carried out with the REE predictive system. The new way of working with temperatures is interpretable as it separates the effect of temperature based on location and time, using variables with a specific meaning. Both studies experimentally demonstrate that the proposed techniques fulfill their purpose, improving the accuracy and computational cost of the predictive system. It is also observed that in Spain heat has a greater influence on demand than cold. On hot days, the temperature of the second previous day has a greater influence than that of the previous one, while on cold days the opposite occurs. Based on the construction of the execution schedule, it has been concluded that temperatures have reduced effect on demand during the early morning hours; temperature forecasts for less than four days ahead provide more accuracy than those more than four; and according as the time difference between the moment of prediction and the moment of execution decreases, the accuracy increases

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid

The objective of this study was to create a tool that will enable renewable energy microgrid (REμG) facility users to make informed decisions on the utilization of electrical power output from a building integrated REμG connected to a smart grid. A decision support tool for renewable energy microgrids (DSTREM) capable of predicting photovoltaic array and wind turbine power outputs was developed. The tool simulated users’ daily electricity consumption costs, avoided CO2 emissions and incurred monetary income relative to the usage of the building integrated REμG connected to the national electricity smart grid. DSTREM forecasted climate variables, which were used to predict REμG power output over a period of seven days. Control logic was used to prioritize supply of electricity to consumers from the renewable energy sources and the national smart grid. Across the evaluated REμG electricity supply options and during working days, electricity exported by the REμG to the national smart grid ranged from 0% to 61% of total daily generation. The results demonstrated that both monetary saving and CO2 offsets can be substantially improved through the application of DSTREM to a REμG connected to a building

Forecasting summer-time overheating in UK homes using time series models

Heatwaves are projected to become more frequent, intense and long-lasting in the UK and the prevalence of overheating in dwellings is set to increase. As a result, occupants will experience increased levels of thermal discomfort, heat stress and heat-related morbidity and mortality. Since the use of mechanical air conditioning in dwellings is unsustainable, and not widely affordable, it is of utmost importance to understand when heat related health risks are anticipated in free-running dwellings. This is crucial for vulnerable occupants, such as the elderly, for whom the accurate detection of future heat risks could prepare them (or their carers) for timely mitigation, for example, through additional window ventilation or the use of shading. Many countries deploy Heat-Health Warning Systems (HHWS) to alert their populations, however, these generally apply to a wide area and are based exclusively on regional weather forecasts. Consequently, HHWSs are unable to identify where, when, or to what extent individual buildings (and their occupants) will be affected. Previous studies have investigated the use of time series forecasting models, with the majority considering the use of Model Predictive Control. There is, however, no rigorous scientific evidence to support the belief that such models can provide accurate predictions in free-running dwellings during heatwaves and over multi-day forecasting horizons. This thesis therefore examines the use of black-box forecasting models to provide reliable predictions of the impending indoor temperatures in UK homes. Having established the viability of this approach, the application of such models in the context of an indoor Heat-Health Warning System (iHHWS) has been explored. This research led to five main findings: (i) linear AutoRegressive forecasting models with eXogenous inputs (ARX), i.e. weather forecasts, can provide satisfactory accuracies during heatwaves for time horizons up to 72 h ahead; (ii) more complex semi-parametric Generalized Additive Models (GAMs) were not capable of significantly improving the forecasting accuracy at forecasting horizons over 6 h (iii) logistic GAMs can predict the window opening state with adequate discrimination, however, integration of the window state into forecasting models did not improve their accuracy; (iv) forecasting models could be usefully incorporated within an iHHWS, however, the warning lead-time should be constrained to less than 24 h in order to guarantee high confidence in such a system; (v) a weighted metric such as the Cumulative Heat Index (CHI) could further reduce the risks of false or missed warnings, increasing the dependability of the iHHWS.</div

Holistic modelling techniques for the operational optimisation of multi-vector energy systems

Modern district energy systems are highly complex with several controllable and uncontrollable variables. To effectively manage a multi-vector district requires a holistic perspective in terms of both modelling and optimisation. Current district optimisation strategies found in the literature often consider very simple models for energy generation and conversion technologies. To improve upon the state of the art, more realistic and accurate models must be produced whilst remaining computationally and mathematically simple enough to complete within short periods. Therefore, this paper provides a comprehensive review of modelling techniques for common district energy conversion technologies including Power-to-Gas. In addition, dynamic building modelling techniques are reviewed as buildings must be considered active and flexible participants in a district energy system. In both cases, a specific focus is placed on artificial intelligence-based models suitable for implementation in the real-time operational optimisation of multi-vector systems. Future research directions identified from this review include the need to integrate simplified models of energy conversion units, energy distribution networks, dynamic building models and energy storage into a holistic district optimisation. Finally, a future district energy management solution is proposed. It leverages semantic modelling to allow interoperability of heterogeneous data sources to provide added value inferencing from contextually enriched informatio

Emerging Technologies for the Energy Systems of the Future

Energy systems are transiting from conventional energy systems to modernized and smart energy systems. This Special Issue covers new advances in the emerging technologies for modern energy systems from both technical and management perspectives. In modern energy systems, an integrated and systematic view of different energy systems, from local energy systems and islands to national and multi-national energy hubs, is important. From the customer perspective, a modern energy system is required to have more intelligent appliances and smart customer services. In addition, customers require the provision of more useful information and control options. Another challenge for the energy systems of the future is the increased penetration of renewable energy sources. Hence, new operation and planning tools are required for hosting renewable energy sources as much as possible