Forecasting daily urban electric load profiles using artificial neural networks

This paper presents the development of Short Term Load Forecasting (STLF) model using Artificial Neural Network (ANN). STLF is required for electric power planning and electricity market planning. The proposed model predicts the load demand of Connecticut in the U.S. using hourly historical electric load and weather data. For improving the load prediction accuracy, we consider two main issues that are seasons and weather factors. Each season has different load demand patterns, thus the weather factors are differently applied in each season. The proposed model uses the composited weather factor which consists of temperature and dew point. The temperature and dew point weather factors are selected through the correlation coefficient to obtain the meaningful data among the weather factors. The selected weather factors adjust the level of the pitch which is the predicted load demand of one day ahead. The proposed model improves the forecasting accuracy both in summer and winter.

The paper illustrates a part of the research activity conducted by authors in the field of electric Short Term Load Forecasting (STLF) based on Artificial Neural Network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architectures provide results comparable with those obtained by human operators for most normal days, they evidence some accuracy deficiencies when applied to ''anomalous'' load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen's Self Organizing Map (SOM) The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer perceptron with a backpropagation learning algorithm similar to the ones above mentioned. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations.

This research paper investigates the application of artificial neural networks (ANNs) and fuzzy logic methods for short-term load forecasting in the Western Libyan Electric Network. Historical hourly load data collected during the year 2023 by the General Electricity Company of Libya (GECOL). The study aims to develop accurate and reliable load forecasting models to support efficient grid operation and resource planning. Historical load data and meteorological variables are utilized to train and evaluate the forecasting models. The performance of the ANNs and fuzzy logic methods is compared, and the suitability of each approach for load forecasting in the Western Libyan Electric Network is assessed. The results demonstrate the effectiveness of both ANNs and fuzzy logic methods in short-term load forecasting, providing valuable insights for electricity providers and policymakers in the region. Keywords: neural' networks, fuzzy logic, load forecasting

An application of artificial neural networks (ANNs) to short-term load forecasting is presented in this paper. An algorithm using cascaded learning together with historical load and weather data is proposed to forecast half-hourly power system load for the next 24 hours. This cascaded neural network algorithm (CANNs) includes peak, minimum and daily energy prediction as additional input data for the final forecast stage. These additional input data are predicted using the first (ANNs) model. The networks are trained and tested on the electric power system of Kuwait. The absolute average forecasting error is reduced from 3.367% to 2.707% by applying CANNs as compared to the conventional ANNs. Simulation results indicate that the developed forecasting approach is effective and point to the potential of the methodology for economic applications.

Information about the availability of solar radiation on horizontal surface is essential for the optimum design and study of solar energy systems. For a country like UAE, the efficient application of solar energy seems inevitable because of abundant sunshine available throughout the year. The traditional way of knowing the amount of global solar radiation (GSR) in a particular region is to install pyranometers at as many locations as possible in this region thus requiring daily maintenance and data recording, and consequently increasing cost of GSR data collection. Therefore, it is rather more economical to develop methods to estimate the GSR using climatological parameters (Akhlaque et al., 2009; Kassem et al., 2009; Falayi et al., 2008; El-Sebaii & Trabea, 2005). Many researchers estimated global solar radiation by using artificial neural networks. (Mohandes et al, 1998) applied ANN techniques to predict GSR using weather data from 41 stations in Saudi Arabia. Data from 31 stations was used in training the NN and the remaining data was used for testing. Input variables to the NN included 4 parameters: latitude, longitude, altitude and sunshine duration. Their sample data was not large enough to allow a credible comparison between the ANN models used and empirical regression models. (Lam et al., 2008) have used Artificial Neural networks ANNs to develop predication models for daily global solar radiation using measured sunshine duration for 40 cities covering 9 major thermal climatic zones and sub-zones in China. (Alam et al., 2009) used ANNs to estimate monthly mean hourly and daily diffuse solar radiation based on weather data from 10 Indian stations which have different climatic conditions. (Alawi & Hinai, 1998) applied ANNs to predict solar radiation in areas not covered by direct measurement instrumentation. The input data that was used for building the network were the location, month, mean pressure, mean temperature, mean vapor pressure, mean relative humidity, mean wind speed and mean duration of sunshine. (Tasadduq et al., 2002) have used neural networks for the prediction of hourly mean values of ambient temperature 24 hours in advance. Full year hourly values of ambient temperature are used to train a neural network model for a coastal location — Jeddah, Saudi Arabia. (Elminir et al., 2005) applied ANN modeling techniques to predict solar radiation data in different spectrum bands from data of meteorology for Helwan (Egypt) meteorology monitoring station. (Rehman & Mohandes, 2008) developed ANN-based estimation GSR for Abha city in Saudi Arabia; by

Short-term load forecasting plays a vital role for the power system’s safe operation and production arrangements. This paper regards electric load forecasting as a nonlinear time series prediction problem, and establishes the electric load autoregressive prediction model (ARX Model) based on historical load data, approaching ARX model parameters with RBF neural network, estimating model parameters with using a Structured Nonlinear Parameters Optimization Method (SNPOM), proposing cycle prediction method for short-term electric load forecasting. Experimental results show that the method has higher prediction accuracy for short-term electric load forecasting. Keywords: Short-term load forecasting; time series; RBF-ARX model; cycle forecasting; structured nonlinear parameter optimization method

Electricity load forecast needs to ensure minimum load wastage and requires intelligent decision-making systems to accurately predict future load demand. Learning capability, robustness and ability to solve nonlinear problems make ANN widely acceptable. For accurate short-term load forecast (STLF), an ensemble soft computing approach, namely ANN-PSOHm, composed of an artificial neural network (ANN) and particle swarm optimization (PSO) with homeostasis based mutation is presented in this article. To enhance the learning strength of ANN, PSO undergoes homeostasis mutation to bring greater diversity among solutions by exploring in wider search space. To demonstrate the effectiveness of ANN, three case studies on load data of NEPOOL region (courtesy ISO New England) are performed. The experimental results show that ANN-PSOHm improves accuracy by 11.57% MAPE over ANN-PSO.

In this paper, a method based on “artificial neural network” is proposed for electric load prediction. The prediction of load for the next hour to several days out so-called short term load forecasting is one of the most important requirements for the operation and planning activities of an electrical utility. Presented method in this paper is the development of an artificial neural network based short-term load forecasting model. The model can forecast daily load profiles with a load time of one day for the next 24 h. Days of year with using average temperature are divided in this method. In addition, groups are divided according to the linearity rate of curve. Then, with considering weekday and weekend, the ultimate forecast load is obtained for each group. Moreover, an investigation is made on the effects of temperature and humidity on consuming curve. The results indicates the forecasting load curve of holidays at first forecast pick and valley and then the neural network forecast is re-shaped with the new data. The ANN-based load models are trained using hourly historical, load data and daily historical max/min temperature and humidity data. The results of testing the system on data from Yazd utility are reported. Key words: Artificial neural network, forecasting, short-term load.

Short term Load Forecasting (STLF) has become an important feature of the ‘smart grid’ concept. Though there has been considerable efforts, especially in the aggregated load aspect, luck of a clear-cut recommendation on single method of forecasting due to the high irregularity of load patterns and variation of load characteristics of different consumer types has made it still a topic that needs further study. In this study, an approach composed of wavelet transform and autoregressive neural networks is devised and tested to prove an improved forecast for single commercial type customer. The method proposes breaking down the load data into an approximate and detail components through wavelet decomposition that will be treated and forecasted independently. A unique step of determining the optimal feedback delays for each target parameters through autocorrelation analysis is adopted. A cross correlation analysis is employed to decide on the usage of external input parameters and hence choose either of the NARnet or NARXnet models. The individual forecasts are later merged trough wavelet reconstruction to give the load forecast. The technique was tested using practical load and weather data. The method showed high effectiveness compared to NAR and NARX networks as well as SVM and ANFIS techniques.

The paper addresses the problem of short term load forecasting (STLF) using Artificial Neural Network (ANN) and Ensemble models based on Bootstrap Aggregation considering the influence of solar irradiance on the load pattern. STLF has a remarkable role in the process of power scheduling. It helps power engineers in several decision making processes associated with planning of supply, spinning reserve, security of power system, scheduling dispatching, demand-side management, financial planning. Moreover with the emergence of concepts like smart grid and restructuring of energy market STLF has gained increased attention as it is essential for the time-ahead operation of power market. Thus in this paper, STLF models based on ANN (Artificial Neural Network) and Ensemble Method (Bootstrap Aggregation model) are investigated and compared. The study is carried out on MATLAB platform and out performance of load predicting models are analyzed and compared on the basis of parameters like MAPE, MAE and daily peak forecast error.

Short-term load forecasting is very important for power system dispatching since the economy and stability of power system are affected by the accuracy of load forecasting. Therefore, a short-term load forecasting method based on deep belief neural network (DBN) is proposed in this paper. Firstly, the low-voltage user load data are normalized to construct the training set and test set for low-voltage user short-term load forecasting; then, the DBN model is trained and adjusted by using the training set to obtain the well-trained DBN network; finally, the historical data of short-term load of users are input for load forecasting. The actual load data in Zhejiang power grid of China are serviced for demonstrating the effectiveness the proposed algorithm. The simulation results show that the short-term load forecasting algorithm proposed in this paper is with high accuracy for the actual load forecasting of low voltage users.

Short-term load forecasting is an essential instrument in power system planning, operation & control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis & maintenance planning for the generators. This paper discusses significant role of artificial intelligence (AI) in short-term load forecasting (STLF), that is, the one hour-ahead forecast of the power system load. A new artificial neural network (ANN) has been designed to compute the forecasted load. Historical electricity load data has been used in the modeling of ANN. The ANN model is trained on hourly data from PJM Electricity Market & UPPCL and tested on out-of-sample data. Simulation results obtained have shown that one hour-ahead forecasts of load using proposed ANN is very accurate with very less error.

Artificial neural network and regression coupled genetic algorithm to optimize parameters for enhanced xylitol production by Debaryomyces nepalensis in bioreactor

Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment

This paper presented a hybrid neural network model to integrate information entropy theory and ant colony clustering for load forecasting. As short-term load forecasting is a complex problem with multifactor in power system, if all these factors are used as inputs of neural network, it will not only result in complicated network structure, but also long learning time and inaccurate prediction. First, information entropy theory is used to select relevant ones from all load influential factors, the results are used as inputs of neural network. It can reduce irrelevant load influential factors and the input variables of the input layer for neural network. Next, considering the features of power load and reduced influential factors, using ant colony clustering method, the practical historical load data within one year is divided into several groups. A separate module based on neural networks models each group. Then, the typical samples in each clustered group were selected as the training set for the separate improved Elman neural network which is a kind of globally feed forward locally recurrent network model with distinguished dynamical characteristics. According to the procedures, the reduced input variables and the typical training samples for each neural network can be gotten. Thus the neural network forecasting model based on information entropy and ant colony clustering can be constructed which can effectively reduce the training time and improve convergent speed. During the forecasting process, pattern recognizing is employed to activate the corresponding module for hourly load forecasting. The presented model was tested using Hebei Province daily load data, and the satisfactory results were obtained.