A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen

Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

Performance Evaluation of Different Optimization Algorithms for Power Demand Forecasting Applications in a Smart Grid Environment

AbstractThis paper presents an in-depth performance evaluation of three different optimization algorithms, in particular genetic algorithm (GA), particle swarm optimization (PSO), and firefly (FF) algorithm for power demand forecasting in a deregulated electricity market and smart grid environments. In this framework, this paper proposes a hybrid intelligent algorithm for power demand forecasts using the combination of wavelet transform (WT) and fuzzy ARTMAP (FA) network that is optimized by using FF optimization algorithm. The effectiveness and accuracy of the proposed hybrid WT+FF+FA model is trained and tested utilizing the data obtained from ISO-NE electricity market

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of one week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. © 2011 Elsevier Ltd. All rights reserved

An Overview of Electricity Demand Forecasting Techniques

Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. This paper presents a review of electricity demand forecasting techniques. The various types of methodologies and models are included in the literature. Load forecasting can be broadly divided into three categories: short-term forecasts which are usually from one hour to one week, medium forecasts which are usually from a week to a year, and long-term forecasts which are longer than a year.  Based on the various types of studies presented in these papers, the load forecasting techniques may be presented in three major groups: Traditional Forecasting technique, Modified Traditional Technique and Soft Computing Technique. Keywords: Electricity Demand, Forecasting Techniques, Soft Computing, Regression method, SVM

Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting

Forecasting price has now become essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increasing the utility’s profit and energy efficiency as well. The main challenge in forecasting electricity price is when dealing with non-stationary and high volatile price series. Some of the factors influencing this volatility are load behavior, weather, fuel price and transaction of import and export due to long term contract. This paper proposes the use of Least Square Support Vector Machine (LSSVM) with Genetic Algorithm (GA) optimization technique to predict daily electricity prices in Ontario. The selection of input data and LSSVM’s parameter held by GA are proven to improve accuracy as well as efficiency of prediction. A comparative study of proposed approach with other techniques and previous research was conducted in term of forecast accuracy, where the results indicate that (1) the LSSVM with GA outperforms other methods of LSSVM and Neural Network (NN), (2) the optimization algorithm of GA gives better accuracy than Particle Swarm Optimization (PSO) and cross validation. However, future study should emphasize on improving forecast accuracy during spike event since Ontario power market is reported as among the most volatile market worldwide

Hybrid artificial intelligence algorithms for short-term load and price forecasting in competitive electric markets

The liberalization and deregulation of electric markets forced the various participants to accommodate several challenges, including: a considerable accumulation of new generation capacity from renewable sources (fundamentally wind energy), the unpredictability associated with these new forms of generation and new consumption patterns, contributing to further electricity prices volatility (e.g. the Iberian market). Given the competitive framework in which market participants operate, the existence of efficient computational forecasting techniques is a distinctive factor. Based on these forecasts a suitable bidding strategy and an effective generation systems operation planning is achieved, together with an improved installed transmission capacity exploitation, results in maximized profits, all this contributing to a better energy resources utilization. This dissertation presents a new hybrid method for load and electricity prices forecasting, for one day ahead time horizon. The optimization scheme presented in this method, combines the efforts from different techniques, notably artificial neural networks, several optimization algorithms and wavelet transform. The method’s validation was made using different real case studies. The subsequent comparison (accuracy wise) with published results, in reference journals, validated the proposed hybrid method suitability.O processo de liberalização e desregulação dos mercados de energia elétrica, obrigou os diversos participantes a acomodar uma série de desafios, entre os quais: a acumulação considerável de nova capacidade de geração proveniente de origem renovável (fundamentalmente energia eólica), a imprevisibilidade associada a estas novas formas de geração e novos padrões de consumo. Resultando num aumento da volatilidade associada aos preços de energia elétrica (como é exemplo o mercado ibérico). Dado o quadro competitivo em que os agentes de mercado operam, a existência de técnicas computacionais de previsão eficientes, constituí um fator diferenciador. É com base nestas previsões que se definem estratégias de licitação e se efetua um planeamento da operação eficaz dos sistemas de geração que, em conjunto com um melhor aproveitamento da capacidade de transmissão instalada, permite maximizar os lucros, realizando ao mesmo tempo um melhor aproveitamento dos recursos energéticos. Esta dissertação apresenta um novo método híbrido para a previsão da carga e dos preços da energia elétrica, para um horizonte temporal a 24 horas. O método baseia-se num esquema de otimização que reúne os esforços de diferentes técnicas, nomeadamente redes neuronais artificiais, diversos algoritmos de otimização e da transformada de wavelet. A validação do método foi feita em diferentes casos de estudo reais. A posterior comparação com resultados já publicados em revistas de referência, revelou um excelente desempenho do método hibrido proposto