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 hourly load forecasting in South Africa using neutral networks

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science, Johannesburg, 30 March 2018.Accuracy of the load forecasts is very critical in the power system industry, which is the lifeblood of the global economy to such an extent that its art-of-the-state management is the focus of the Short-Term Load Forecasting (STLF) models. In the past few years, South Africa faced an unprecedented energy management crisis that could be addressed in advance, inter alia, by carefully forecasting the expected load demand. Moreover, inaccurate or erroneous forecasts may result in either costly over-scheduling or adventurous under-scheduling of energy that may induce heavy economic forfeits to power companies. Therefore, accurate and reliable models are critically needed. Traditional statistical methods have been used in STLF but they have limited capacity to address nonlinearity and non-stationarity of electric loads. Also, such traditional methods cannot adapt to abrupt weather changes, thus they failed to produce reliable load forecasts in many situations. In this research report, we built a STLF model using Artificial Neural Networks (ANNs) to address the accuracy problem in this field so as to assist energy management decisions makers to run efficiently and economically their daily operations. ANNs are a mathematical tool that imitate the biological neural network and produces very accurate outputs. The built model is based on the Multilayer Perceptron (MLP), which is a class of feedforward ANNs using the backpropagation (BP) algorithm as its training algorithm, to produce accurate hourly load forecasts. We compared the MLP built model to a benchmark Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) model using data from Eskom, a South African public utility. Results showed that the MLP model, with percentage error of 0.50%, in terms of the MAPE, outperformed the SARIMAX with 1.90% error performance.LG201

Forecasting Strategies for Predicting Peak Electric Load Days

Academic institutions spend thousands of dollars every month on their electric power consumption. Some of these institutions follow a demand charges pricing structure; here the amount a customer pays to the utility is decided based on the total energy consumed during the month, with an additional charge based on the highest average power load required by the customer over a moving window of time as decided by the utility. Therefore, it is crucial for these institutions to minimize the time periods where a high amount of electric load is demanded over a short duration of time. In order to reduce the peak loads and have more uniform energy consumption, it is imperative to predict when these peaks occur, so that appropriate mitigation strategies can be developed. The research work presented in this thesis has been conducted for Rochester Institute of Technology (RIT), where the demand charges are decided based on a 15 minute sliding window panned over the entire month. This case study makes use of different statistical and machine learning algorithms to develop a forecasting strategy for predicting the peak electric load days of the month. The proposed strategy was tested for a whole year starting May 2015 to April 2016 during which a total of 57 peak days were observed. The model predicted a total of 74 peak days during this period, 40 of these cases were true positives, hence achieving an accuracy level of 70 percent. The results obtained with the proposed forecasting strategy are promising and demonstrate an annual savings potential worth about $80,000 for a single submeter of RIT

A technique for determining viable military logistics support alternatives

A look at today's US military will see them operating much beyond the scope of protecting and defending the United States. These operations now consist of, but are not limited to humanitarian aid, disaster relief, and conflict resolution. This broad spectrum of operational environments has necessitated a transformation of the individual military services into a hybrid force that can leverage the inherent and emerging capabilities from the strengths of those under the umbrella of the Department of Defense (DOD), this concept has been coined Joint Operations. Supporting Joint Operations requires a new approach to determining a viable military logistics support system. The logistics architecture for these operations has to accommodate scale, time, varied mission objectives, and imperfect information. Compounding the problem is the human in the loop (HITL) decision maker (DM) who is a necessary component for quickly assessing and planning logistics support activities. Past outcomes are not necessarily good indicators of future results, but they can provide a reasonable starting point for planning and prediction of specific needs for future requirements. Adequately forecasting the necessary logistical support structure and commodities needed for any resource intensive environment has progressed well beyond stable demand assumptions to one in which dynamic and nonlinear environments can be captured with some degree of fidelity and accuracy. While these advances are important, a holistic approach that allows exploration of the operational environment or design space does not exist to guide the military logistician in a methodical way to support military forecasting activities. To bridge this capability gap, a method called A Technique for Logistics Architecture Selection (ATLAS) has been developed. This thesis describes and applies the ATLAS method to a notional military scenario that involves the Navy concept of Seabasing and the Marine Corps concept of Distributed Operations applied to a platoon sized element. This work uses modeling and simulation to incorporate expert opinion and knowledge of military operations, dynamic reasoning methods, and certainty analysis to create a decisions support system (DSS) that can be used to provide the DM an enhanced view of the logistics environment and variables that impact specific measures of effectiveness.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Fahringer, Philip; Committee Member: Nixon, Janel; Committee Member: Schrage, Daniel; Committee Member: Soban, Danielle; Committee Member: Vachtsevanos, Georg

Application of Higher-Order Neural Networks to Financial Time-Series Prediction

Financial time series data is characterized by non-linearities, discontinuities and high frequency, multi-polynomial components. Not surprisingly, conventional Artificial Neural Networks (ANNs) have difficulty in modelling such complex data. A more appropriate approach is to apply Higher-Order ANNs, which are capable of extracting higher order polynomial coefficients in the data. Moreover, since there is a one-to-one correspondence between network weights and polynomial coefficients, HONNs (unlike ANNs generally) can be considered open-, rather than 'closed box' solutions, and thus hold more appeal to the financial community. After developing Polynomial and Trigonometric HONNs, we introduce the concept of HONN groups. The latter incorporate piecewise continuous activation functions and thresholds, and as a result are capable of modelling discontinuous (piecewise continuous) data, and what's more to any degree of accuracy. Several other PHONN variants are also described. The performance of P(T)HONNs and HONN groups on representative financial time series is described (credit ratings and exchange rates). In short, HONNs offer roughly twice the performance of MLP/BP on financial time series prediction, and HONN groups around 10% further improvement

AI-driven approaches for optimizing the energy efficiency of integrated energy system

To decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions. This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue. The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion

A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST

Electrical load forecast is an important part of the power system energy management system. Reliable load forecast technique will help the electric utility to make unit commitment decisions, reduce spinning reserve capacity, and schedule device maintenance plan properly. Thus, besides being a key element in reducing the generation cost, power load forecast is an essential procedure in enhancing the reliability of the power systems. Generally speaking, power systems worldwide are using load forecast as an essential part of off-line network analysis. This is in order to determine the status of the system, and the necessity to implement corrective actions, such as load shedding, power purchases or using peaking units. Short term load forecast (STLF), in terms of one-hour ahead, 24-hours ahead, and 168-hours ahead is a necessary daily task for power dispatch. Its accuracy will significantly affect the cost of generation and the reliability of the system. The majority of the single variable based techniques are using autoregressive-moving average (ARMA) model to solve the STLF problem. In this thesis, a new AR algorithm especially designed for long data records as a solution to STLF problem is proposed. The proposed AR-based algorithm divides long data record into short segments and searches for the AR coefficients that simultaneously model the data with the least means squared errors. In order to verify the proposed algorithm as a solution to STLF problem, its performance is compared with other AR-based algorithms, like Burg and the seasonal Box-Jenkins ARIMA (SARIMA). In addition to the parametric algorithms, the comparison is extended towards artificial neural networks (ANN). Three years data power demand record collected by NEMMCO in four Australian states, NSW, QLD, SA, and VIC, between the beginning of 2005 and the end of 2007 are used for the comparison. The results show the potential of the proposed algorithm as a reliable solution to STLF

FLANN Based Model to Predict Stock Price Movements of Stock Indices

Financial Forecasting or specifically Stock Market prediction is one of the hottest fields of research lately due to its commercial applications owing to the high stakes and the kinds of attractive benefits that it has to offer. Forecasting the price movements in stock markets has been a major challenge for common investors, businesses, brokers and speculators. As more and more money is being invested the investors get anxious of the future trends of the stock prices in the market. The primary area of concern is to determine the appropriate time to buy, hold or sell. In their quest to forecast, the investors assume that the future trends in the stock market are based at least in part on present and past events and data [1]. However financial time-series is one of the most ‘noisiest’ and ‘non-stationary’ signals present and hence very difficult to forecas