Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Energy models for demand forecasting—A review

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs.

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Forecasting methods in energy planning models

A review of wind speed and wind power forecasting with deep neural networks

Fault diagnosis of electric impact drills using thermal imaging

A novel gas turbine fault diagnosis method based on transfer learning with CNN

A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting

Short-term wind speed prediction model based on GA-ANN improved by VMD

A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic

Accurate wind power prediction provides significant guarantee for power grid dispatching, and wind speed prediction, as the basic link of wind power forecasting, has crucial theoretical research significance and practical application value In this paper, we present the wind speed prediction of IPSO-BP neural network based on Lorenz disturbance At first, the data is processed by principal component analysis (PCA) to select the key factors affecting wind speed, which can effectively reduce the complexity of model Then, the improved particle swarm optimization (IPSO) algorithm is used to globally optimize the weights and thresholds of BP neural network, and overcome the problem of local minimum value The initial prediction results can be obtained by the IPSO-BPNN model Finally, Lorenz system is introduced to correct the initial prediction value and improve forecasting accuracy According to the wind farm data of Spain and Chang Ma in China, we take an empirical research to analyze the optimization effect of IPSO algorithm and the promotion effect of Lorenz system on the precision of preliminary forecasting The results are as follows: 1) IPSO algorithm accelerates the convergence rate of weights and thresholds of BP neural network and 2) Lorenz disturbance system obviously weakens the random volatility of wind speed, effectively modifies its preliminary prediction results, and upgrades its prediction accuracy

Wind Speed Prediction of IPSO-BP Neural Network Based on Lorenz Disturbance

Wind speed is one of the fundamental influences affecting wind power uncertainties, and these uncertainties influence the stability of wind power grid. Therefore, to quantify the uncertainty fluctuation risk of wind power and reduce the uncertainty in the process of correcting wind speed, the wind speed interval prediction based on Lorenz disturbance distribution is presented. First, the point prediction of wind speed is obtained by an RBF neural network and empirical mode decomposition. Then, Lorenz disturbance sequence (LDS) with nonlinear and strong fluctuation is introduced, and the probability density distribution of LDS is estimated by Bootstrap and kernel density method. Next, the probability density distribution of LDS and quantile regression is, respectively, adopted to carry out wind speed interval estimation. Finally, comparing the reliability indexes and sensitivity indexes of those methods, the validity and stability of the proposed method can be verified. The results show that wind speed interval prediction based on the distribution of LDS can describe the fluctuation rage and tendency of wind speed effectively, and its result is stabler than that of quantile regression. In this research, the major differences between this paper and recently published manuscripts lie in introducing the distribution of LDS to make wind speed interval prediction; LDS can quantify the uncertain factors in the atmosphere and to reduce the uncertainties of prediction process, predicted interval of wind speed can provide more scientific reference for wind turbine to make start–stop scheme, which helps to quantify uncertain risk of wind power and ensure the stable operation of power system.

Yuan Zhao

Papers

A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting

Short-term wind speed prediction model based on GA-ANN improved by VMD

A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic

Wind Speed Prediction of IPSO-BP Neural Network Based on Lorenz Disturbance

Wind Speed Interval Prediction Based on Lorenz Disturbance Distribution