Mid-Term Load Forecasting: Level Suitably of Wavelet and Neural Network based on Factor Selection

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

doi:10.1016/j.egypro.2011.12.955

Cited by 23 publications

(10 citation statements)

References 3 publications

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“…Up to now, several hybrid prediction models have been proposed such as combined ARIMA-support vector machine (SVM) [20][21][22], hybrid of Grey and Box-Jenkins autoregressive moving average (ARMA) models [23], hybrid of ARIMA and fuzzy logic [24], hybrid of support vector regression (SVR) and differential evolution (DE) algorithm [25], integrated ANN-genetic algorithms (GAs) [26][27][28]/ANN-particle swarm optimization (PSO) [14]/ ANN-artificial fish swarm algorithm (AFSA) [29], combined generalized linear autoregression (GLAR)-ANN [30], hybrid of artificial intelligence (AI) and ANN [31], hybrid of wavelets and ANN implemented on a decision support system [32,33], integrated ARMA-ANN [34,35], combined seasonal ARIMA-back propagation (BP) ANN [36], combination of several ANNs [15,37], hybrid model of self organization map (SOM) neural network, GAs, and fuzzy rule base (FRB) [38], combined fuzzy techniques-ANN [39][40][41], hybrid based on PSO, evolutionary algorithm (EA) and DE for training a recurrent neural network (RNN) [42].…”

Section: Introductionmentioning

confidence: 99%

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Sheikhan

Mohammadi

2012

Neural Comput & Applic

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Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field.

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Section: Introductionmentioning

confidence: 99%

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Sheikhan

Mohammadi

2012

Neural Comput & Applic

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“…Other authors [13][14][15] propose methods for medium-term forecasts. This approach is proposed in order to minimize the error in the demand forecast.…”

Section: Stationary Forecasting Modelsmentioning

confidence: 99%

Solar Energy Production for a Decarbonization Scenario in Spain

2019

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Solar energy is one of the most promising sources of energy that could be used to address distributed supply problems. Global warming and decarbonization are significant global concerns, particularly for countries that are not fossil fuel providers. This paper presents a study focused on Spain, a country with a favorable location with respect to horizontal irradiance. The study addresses the future energy demand forecast and how photovoltaic energy could supply an important part of electricity needs. Our approach focuses on two analyses. First, several traditional statistical techniques are discussed in order to obtain a model that best suits Spanish energy demand forecasts for the future years. Different algorithms are compared in order to determine which is the most appropriate for the considered purpose. Second, the evolution of solar photovoltaic technology in Spain is analyzed. The latitude of Spanish cities makes them suitable for utilizing this kind of technology. In this sense, seasonal and monthly trends are identified with high levels of detail, considering a large historical dataset. The increase of the capacity of electricity generation based on this procedure is evaluated. Finally, a discussion about matching electricity demand forecasts and photovoltaic production is offered. Considering the selected model for the photovoltaic power of Spain, from 5 to 42 GW in 2030, the Spanish production is determined as 81 TWh. The obtained results suggest that a possible energy transition is feasible. However, some challenges have to be considered, such us the design of an effective strategy to store excess energy produced when generation is higher than electricity demand. In this way, the electrical distribution system could be fed by the stored energy when solar energy production is deficient.

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“…The load demand of a country or region depends on two major factors, the complexity of the economy and the weather of the area [8]. Studies have revealed that a large proportion of the variability in electricity demand is dependent on weather variables such as air temperature, humidity, wind speed, cloud cover and luminosity [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…In the past, several artificial intelligence methods like ANNs, genetic algorithms, fuzzy logic, fuzzy expert systems, self-organising maps, wavelet transform, principal component analysis, grey system theory and support vector regression have been developed for forecasting electricity demand [8,9,[12][13][14] Most of these methods are based on large datasets of historical time series of load data. In some cases, in addition to historical load data, both weather variables (temperature, relative humidity, wind velocity and cloudiness) and socio-economic factors have been used as inputs to the forecasting model [11].…”

Section: Introductionmentioning

confidence: 99%

Generation of synthetic benchmark electrical load profiles using publicly available load and weather data

Pillai¹,

Putrus²,

Pearsall³

2014

International Journal of Electrical Power & Energy Systems

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Electrical load profiles of a particular region are usually required in order to study the performance of renewable energy technologies and the impact of different operational strategies on the power grid. Load profiles are generally constructed based on measurements and load research surveys which are capital and labour-intensive.In the absence of true load profiles, synthetically generated load profiles can be a viable alternative to be used as benchmarks for research or renewable energy investment planning. In this paper, the feasibility of using publicly available load and weather data to generate synthetic load profiles is investigated. An Artificial Neural Network (ANN) based method is proposed to synthesize load profiles for a target region using its Typical Meteorological Year 2 (TMY2) weather data as the input. To achieve this, the proposed ANN models are first trained using TMY2 weather data and load profile data of neighbouring regions as the input and targeted output. The limited number of data points in the load profile dataset and the consequent averaging of TMY2 weather data to match its period resulted in limited data availability for training. This challenge was tackled by incorporating generalization using Bayesian regularization into training. The other major challenge was facilitating ANN extrapolation and this was accomplished by the incorporation of domain knowledge into the input weather data for training. The performance of the proposed technique has been evaluated by simulation studies and tested on three real datasets. Results indicate that the generated synthetic load profiles closely resemble the real ones and therefore can be used as benchmarks.

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Mid-Term Load Forecasting: Level Suitably of Wavelet and Neural Network based on Factor Selection

Cited by 23 publications

References 3 publications

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Solar Energy Production for a Decarbonization Scenario in Spain

Generation of synthetic benchmark electrical load profiles using publicly available load and weather data

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