Electricity demand forecasting using fuzzy hybrid intelligence-based seasonal models

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehdi Khashei ◽  
Fatemeh Chahkoutahi
Keyword(s):  
Electricity Markets ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Multilayer Perceptrons ◽  
Modeling Tools ◽  
Content Type ◽  
Proposed Model ◽  
Hybrid Intelligence ◽  
Electricity Load ◽  
Electricity Load Forecasting

Purpose The purpose of this paper is to propose an extensiveness intelligent hybrid model to short-term load electricity forecast that can simultaneously model the seasonal complicated nonlinear uncertain patterns in the data. For this purpose, a fuzzy seasonal version of the multilayer perceptrons (MLP) is developed. Design/methodology/approach In this paper, an extended fuzzy seasonal version of classic MLP is proposed using basic concepts of seasonal modeling and fuzzy logic. The fundamental goal behind the proposed model is to improve the modeling comprehensiveness of traditional MLP in such a way that they can simultaneously model seasonal and fuzzy patterns and structures, in addition to the regular nonseasonal and crisp patterns and structures. Findings Eventually, the effectiveness and predictive capability of the proposed model are examined and compared with its components and some other models. Empirical results of the electricity load forecasting indicate that the proposed model can achieve more accurate and also lower risk rather than classic MLP and some other fuzzy/nonfuzzy, seasonal nonseasonal, statistical/intelligent models. Originality/value One of the most appropriate modeling tools and widely used techniques for electricity load forecasting is artificial neural networks (ANNs). The popularity of such models comes from their unique advantages such as nonlinearity, universally, generality, self-adaptively and so on. However, despite all benefits of these methods, owing to the specific features of electricity markets and also simultaneously existing different patterns and structures in the electrical data sets, they are insufficient to achieve decided forecasts, lonely. The major weaknesses of ANNs for achieving more accurate, low-risk results are seasonality and uncertainty. In this paper, the ability of the modeling seasonal and uncertain patterns has been added to other unique capabilities of traditional MLP in complex nonlinear patterns modeling.

scholarly journals Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 804-838
Author(s):  
Manogaran Madhiarasan ◽  
Mohamed Louzazni
Keyword(s):  
Neural Network ◽  
Power System ◽  
Multilayer Perceptron ◽  
Load Forecasting ◽  
Consumer Information ◽  
Fine Tuning ◽  
Atmospheric Parameters ◽  
Proposed Model ◽  
Electricity Load ◽  
Electricity Load Forecasting

With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task. This paper proposed a multilayer perceptron neural network (MLPNN) with an association of recursive fine-tuning strategy-based different forecasting horizons model for electricity load forecasting. We consider the atmospheric parameters as the inputs to the proposed model, overcoming the atmospheric effect on electricity load forecasting. Hidden layers and hidden neurons based on performance investigation performed. Analyzed performance of the proposed model with other existing models; the comparative performance investigation reveals that the proposed forecasting model performs rigorous with a minimal evaluation index (mean square error (MSE) of 1.1506 × 10-05 for Dataset 1 and MSE of 4.0142 × 10-07 for Dataset 2 concern to the single hidden layer and MSE of 2.9962 × 10-07 for Dataset 1, and MSE of 1.0425 × 10-08 for Dataset 2 concern to two hidden layers based proposed model) and compared to the considered existing models. The proposed neural network possesses a good forecasting ability because we develop based on various atmospheric parameters as the input variables, which overcomes the variance. It has a generic performance capability for electricity load forecasting. The proposed model is robust and more reliable.

scholarly journals Electricity load forecasting: a systematic review

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Isaac Kofi Nti ◽  
Moses Teimeh ◽  
Owusu Nyarko-Boateng ◽  
Adebayo Felix Adekoya
Keyword(s):  
Energy Consumption ◽  
Demand Forecasting ◽  
Research Work ◽  
Load Forecasting ◽  
Electrical Energy ◽  
Electricity Demand ◽  
Electricity Demand Forecasting ◽  
Global Demand ◽  
Electricity Load ◽  
Electricity Load Forecasting

Abstract The economic growth of every nation is highly related to its electricity infrastructure, network, and availability since electricity has become the central part of everyday life in this modern world. Hence, the global demand for electricity for residential and commercial purposes has seen an incredible increase. On the other side, electricity prices keep fluctuating over the past years and not mentioning the inadequacy in electricity generation to meet global demand. As a solution to this, numerous studies aimed at estimating future electrical energy demand for residential and commercial purposes to enable electricity generators, distributors, and suppliers to plan effectively ahead and promote energy conservation among the users. Notwithstanding, load forecasting is one of the major problems facing the power industry since the inception of electric power. The current study tried to undertake a systematic and critical review of about seventy-seven (77) relevant previous works reported in academic journals over nine years (2010–2020) in electricity demand forecasting. Specifically, attention was given to the following themes: (i) The forecasting algorithms used and their fitting ability in this field, (ii) the theories and factors affecting electricity consumption and the origin of research work, (iii) the relevant accuracy and error metrics applied in electricity load forecasting, and (iv) the forecasting period. The results revealed that 90% out of the top nine models used in electricity forecasting was artificial intelligence based, with artificial neural network (ANN) representing 28%. In this scope, ANN models were primarily used for short-term electricity forecasting where electrical energy consumption patterns are complicated. Concerning the accuracy metrics used, it was observed that root-mean-square error (RMSE) (38%) was the most used error metric among electricity forecasters, followed by mean absolute percentage error MAPE (35%). The study further revealed that 50% of electricity demand forecasting was based on weather and economic parameters, 8.33% on household lifestyle, 38.33% on historical energy consumption, and 3.33% on stock indices. Finally, we recap the challenges and opportunities for further research in electricity load forecasting locally and globally.

Hybrid online model based multi seasonal decompose for short-term electricity load forecasting using ARIMA and online RNN

2021 ◽  
pp. 1-14
Author(s):  
Nguyen Quang Dat ◽  
Nguyen Thi Ngoc Anh ◽  
Nguyen Nhat Anh ◽  
Vijender Kumar Solanki
Keyword(s):  
Time Series ◽  
Real Time ◽  
Load Forecasting ◽  
Seasonal Patterns ◽  
Time Data ◽  
Short Term ◽  
Proposed Model ◽  
Real Time Data ◽  
Electricity Load ◽  
Electricity Load Forecasting

Short-term electricity load forecasting (STLF) plays a key role in operating the power system of a nation. A challenging problem in STLF is to deal with real-time data. This paper aims to address the problem using a hybrid online model. Online learning methods are becoming essential in STLF because load data often show complex seasonality (daily, weekly, annual) and changing patterns. Online models such as Online AutoRegressive Integrated Moving Average (Online ARIMA) and Online Recurrent neural network (Online RNN) can modify their parameters on the fly to adapt to the changes of real-time data. However, Online RNN alone cannot handle seasonality directly and ARIMA can only handle a single seasonal pattern (Seasonal ARIMA). In this study, we propose a hybrid online model that combines Online ARIMA, Online RNN, and Multi-seasonal decomposition to forecast real-time time series with multiple seasonal patterns. First, we decompose the original time series into three components: trend, seasonality, and residual. The seasonal patterns are modeled using Fourier series. This approach is flexible, allowing us to incorporate multiple periods. For trend and residual components, we employ Online ARIMA and Online RNN respectively to obtain the predictions. We use hourly load data of Vietnam and daily load data of Australia as case studies to verify our proposed model. The experimental results show that our model has better performance than single online models. The proposed model is robust and can be applied in many other fields with real-time time series.

scholarly journals Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Zhongyi Hu ◽  
Yukun Bao ◽  
Tao Xiong
Keyword(s):  
Support Vector Regression ◽  
Electricity Markets ◽  
Load Forecasting ◽  
Solution Space ◽  
Memetic Algorithms ◽  
Pattern Search ◽  
Support Vector ◽  
Forecasting Models ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.

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