Daily Demand Forecasting Research Articles

Urban daily water demand forecasting

Abstract. Problem. The problem of building models and methods of forecasting the daily need for urban water is considered. Much more attention needs to be given to forecasting methods if utilities are to make decisions that reflect the level of uncertainty precisely in future daily demand forecasts. Daily water consumption, unlike annual and monthly water consumption, is much more highly dependent on chance. Goal. The main goal of this paper is to obtain enough accurate forecasts of daily urban water consumption. Method. An algorithm for calculating the urban daily water demand forecast based on the concept of same-type days of water demand for previous years has been suggested. Scientific novelty. The originality of the method lies in the fact that it does not use neural network models, but still makes it possible to obtain enough accurate forecasts of daily urban water needs. Results. The presented algorithm for calculating the urban daily water demand forecast has been implemented in the form of a software package and has been tested for many years in real-life conditions. The average absolute percentage error of the daily forecast of urban water demand for one month does not exceed 5%. Practical significance. The practical value of this work lies in the fact that the presented software complex for calculating the forecast of the city's daily water demand can be used in the information services of city utility companies to make operational and tactical decisions regarding the provision of water supply services to the population.

A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand

In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local service provider's access to information and help them manage their short-term supply more effectively. This study developed the forecasting model using real-time data and weather information from Jeju Island, South Korea. Cluster analysis under the rental pattern of the electric kickboard is a component of the forecasting processes. We cannot achieve noticeable results at first because of the low amount of training data. We require a lot of data to produce a solid prediction result. For the sake of the subsequent experimental procedure, we created synthetic time-series data using a generative adversarial networks (GAN) approach and combined the synthetic data with the original data. The outcomes have shown how the GAN-based synthetic data generation approach has the potential to enhance prediction accuracy. We employ an ensemble model to improve prediction results that cannot be achieved using a single regressor model. It is a weighted combination of several base regression models to one meta-regressor. To anticipate the daily demand in this study, we create an ensemble model by merging three separate base machine learning algorithms, namely CatBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The effectiveness of the suggested strategies was assessed using some evaluation indicators. The forecasting outcomes demonstrate that mixing synthetic data with original data improves the robustness of daily demand forecasting and outperforms other models by generating more agreeable values for suggested assessment measures. The outcomes further show that applying ensemble techniques can reasonably increase the forecasting model's accuracy for daily electric kickboard demand.

Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models

Overnight forecasting is a crucial challenge for revenue managers because of the uncertainty associated between demand and supply. However, there is limited research that focuses on predicting daily hotel demand. Hence, this paper evaluates various models’ of traditional time series forecasting performances for daily demand at multiple horizons. The models include the seasonal naïve, Holt–Winters (HW) triple exponential smoothing, an autoregressive integrated moving average (ARIMA), a seasonal autoregressive integrated moving average (SARIMAX) with exogenous variables, multilayer perceptron (MLP) artificial neural networks model (ANNs), an sGARCH, and GJR-GARCH models. The dataset of this study contains daily demand observations from a hotel in a US metropolitan city from 2015 to 2019 and a set of exogenous social and environmental features such as temperature, holidays, and hotel competitive set ranking. Experimental results indicated that under the MAPE accuracy measure: (i) the SARIMAX model with external regressors outperformed the ANN-MLP model with similar external regressors and the other models, in every one horizon except one out of seven forecast horizons; (ii) the sGARCH(4, 2) and GJR-GARCH(4, 2) shows a superior predictive accuracy at all horizons. The results performance is evaluated by conducting pairwise comparisons between the different model’s distribution of forecasts using Diebold–Mariano and Harvey–Leybourne–Newbold tests. The results are significant for revenue managers because they provide valuable insights into the exogenous variables that impact accurate daily demand forecasting.

Forecasting the short-term urban gas daily demand in winter based on the XGBoost algorithm

The share of natural gas in the primary energy consumption is continuously increasing. The urban gas plays a major part in the natural gas consumption in China, and thus the accurate prediction of the short-term urban gas daily demand in winter facilitates the healthy development of the natural gas consumption and helps to maintain the secure and appropriate urban gas supply. This paper thoroughly analyzed the factors affecting the urban gas demand in winter, introduced the XGBoost algorithm for the forecast, and made accurate predictions regarding the violently-fluctuating short-term urban gas daily demand in winter. In the simulation tests, the Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the forecast made in the proposed model are 8.269 and 1.515%, respectively. Compared with other statistical algorithms and machine learning/artificial intelligence algorithms such as the multivariate regression, random forest and support vector machine, the proposed model has shown better fitting performance and higher prediction accuracy. Therefore, the XGBoost-based prediction model can provide references for the short-term urban gas daily demand forecasting.

Regression model for estimating thermal resource usage in air conditioning systems: daily demand forecast by potential integral method, considering thermal inertia of room and weather conditions

Air conditioning systems are required to achieve both the maintenance of energy conservation and proper control of room environment. Nonetheless, both the building manager and the user (i.e., room resident) are typically ignorant about the amount of air conditioning thermal resources being used by a particular room. Herein, we propose a method to predict the daily demand of thermal resources for individual rooms, considering their thermal characteristics. The amount of thermal resource consumption of individual rooms was predicted. A prediction model of the next-day consumption was also developed. The obtained results indicated that the influence of enthalpy at the end of the previous day was strong, and that the influence of the average temperature of the external factors was small.

Regression model for estimating thermal resource usage in air conditioning systems: daily demand forecast by potential integral method, considering thermal inertia of room and weather conditions

Air conditioning systems are required to achieve both the maintenance of energy conservation and proper control of room environment. Nonetheless, both the building manager and the user (i.e., room resident) are typically ignorant about the amount of air conditioning thermal resources being used by a particular room. Herein, we propose a method to predict the daily demand of thermal resources for individual rooms, considering their thermal characteristics. The amount of thermal resource consumption of individual rooms was predicted. A prediction model of the next-day consumption was also developed. The obtained results indicated that the influence of enthalpy at the end of the previous day was strong, and that the influence of the average temperature of the external factors was small.

Study on Daily Demand Forecasting Orders using Artificial Neural Network

In recent decades, Brazil has undergone several transformations, from a closed economy to a market economy. Transport, processing and distribution of orders remained follow these trends. As a result, the delivery parcel service has become highly complex and competitive. In this context, the forecast demand of orders comes as differential, leading structured productivity and high level of customer service. The paper aims to provide for the daily demand of orders in an Orders Treatment Centre for fifteen days using Artificial Neural Network (ANN). The methodological synthesis of the article is the development of a Artificial Neural Network Multilayer Perceptron (MLP), trained by error back-propagation algorithm. The data for the experiments were collected for 60 days, 45 days to training and 15 days for testing. Experiments were performed with four different topologies of RNA by changing the following parameters: number of hidden layers, number of neurons in the hidden layers, learning rate, momentum rate and stopping criteria. The results obtained with use of RNA in daily demand forecast orders showed good adhesion to the experimental data in the training and testing phases.

Demand Forecasting for Irrigation Water Distribution Systems

One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper exami...