Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks

Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time and travel origin selection, which can be helpful in traffic management. Multiple models and algorithms based on time series prediction and machine learning were applied to this issue and achieved acceptable results. Recently, the availability of sufficient data and computational power, motivates us to improve the prediction accuracy via deep-learning approaches. Recurrent neural networks have become one of the most popular methods for time series forecasting, however, due to the variety of these networks, the question that which type is the most appropriate one for this task remains unsolved. In this paper, we use three kinds of recurrent neural networks including simple RNN units, GRU and LSTM neural network to predict short-term traffic flow. The dataset from TAP30 Corporation is used for building the models and comparing RNNs with several well-known models, such as DEMA, LASSO and XGBoost. The results show that all three types of RNNs outperform the others, however, more simple RNNs such as simple recurrent units and GRU perform work better than LSTM in terms of accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279, arXiv:1804.04176 by other author

Short Term Demand Forecasting for the Integrated Electricity Market

This paper presents a means for the short term load forecasting (STLF) of electricity. The forthcoming Integrated-Single Electricity Market (I-SEM) diverges from the current market structure (the Single Electricity Market or SEM), with significant impacts on Irish supply companies, creating a need for these companies to be able to accurately forecast their customers’ load in the Day Ahead. Using a Double Seasonal Exponential Smoothing variation of the Holt-Winters method that factors in an error correction, data from the Irish market was trained and used to forecast a supply company’s demand resulting in an average daily MAPE (Mean Absolute Percentage Error) of 2.99% over a period of nearly four weeks. The suite of formulas used employs daily and weekly seasonal components to forecast a full day’s (48 half-hour periods) demand

Parametric Time-series Modelling of London Smart Meter Data for Short-term Demand Forecasting

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordElectricity being one of the most important components behind economic growth in 21st century, accurate electricity demand forecast became essential. Now with the deployment of smart meters that are capable of providing half-hour energy usage data comes new opportunities for short-term demand forecasting. In this research two statistical timeseries models known as the seasonal auto-regressive integrated moving average (SARIMA) and with exogenous inputs (SARIMAX) are employed to study half-hourly energy demand forecast and daily peak forecast capability over a week at half-hourly interval. The models are tuned and tested on a half-hourly aggregate level data and individual meters data extracted from London smart-meter dataset. The models are also cross validated over different seasons to evaluate model robustness over different training data size and forecasting under different temperature conditions. The SARIMA model performed better at consistently forecasting daily-demand peaks, while the SARIMAX was overall more accurate as compared to the SARIMA at more computational cost. This is because of the exogenous temperature variable used in SARIMAX which explains some of the demand profile volatility due to temperature changes. This also resulted in a better fit for the SARIMAX model. The models tested in this paper can accurately forecast energy-demand at half-hour intervals and daily-peaks for a week-ahead forecast at a regional demand profile over different seasonal condition.European Regional Development Fund (ERDF

Analisis Pengendalian Risiko Persediaan Bahan Baku Café dengan Short-Term Demand Forecasting (Studi Kasus Di Loodst Coffee, Tulungagung)

Bisnis pangan merupakan salah satu sektor bisnis yang mengalami pertumbuhan cepat di Indonesia, salah satunya adalah café. Loodst Coffee merupakan salah satu café yang telah berkembang di Tulungagung. Meskipun merupakan salah satu café pertama yang ada di Tulungagung, Loodst Coffee masih mengalami permasalahan terutama dalam persediaan bahan baku. Akibat banyaknya jenis bahan baku yang digunakan café serta belum adanya manajemen yang baik, seringkali terjadi risiko berupa jumlah persediaan bahan baku yang tidak sesuai dengan jumlah permintaan. Hal ini menyebabkan beberapa bahan baku sering mengalami kehabisan stok ataupun kelebihan stok. Salah satu alternatif solusi pada permasalahan risiko persediaan bahan baku café adalah dengan melakukan peramalan permintaan bahan baku. Peramalan yang dilakukan menggunakan metode Fuzzy Logic, Artificial Neural Network (ANN) dan Fuzzy Neural Network (FNN) yang merupakan gabungan dari logika fuzzy dengan ANN. Input yang digunakan dalam peramalan berupa data penggunaan bahan baku selama periode Mei-Juni 2017 untuk menghasilkan data peramalan permintaan bahan baku di periode selanjutnya. Untuk mendapatkan hasil peramalan terbaik maka metode yang digunakan akan dibandingkan dengan data aktual permintaan bahan baku café dan menghasilkan Mean Absolute Precentage Error (MAPE). Nilai MAPE yang semakin kecil menunjukkan peramalan yang dilakukan semakin akurat yang artinya dengan metode peramalan tersebut, kemungkinan terjadinya risiko persediaan bahan baku juga semakin kecil dan begitu pula sebaliknya. Berdasarkan peramalan yang telah dilakukan, peramalan terbaik diperoleh dengan metode ANN yang menghasilkan nilai MAPE antara 1-55%, sedangkan metoden FNN menghasilkan MAPE 16-67% dan metode Fuzzy Logic menghasilkan MAPE 21-73%

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

Short-Term Power Demand Forecasting Using Blockchain-Based Neural Networks Models

With the rapid development of blockchain technology, blockchain-based neural network short-term power demand forecasting has become a research hotspot in the power industry. This paper aims to combine neural network algorithms with blockchain technology to establish a trustworthy and efficient short-term demand forecasting model. By leveraging the distributed ledger and immutability features of blockchain, we ensure the security and reliability of power demand data. Meanwhile, short-term power demand forecasting research using neural networks has the potential to increase the stability of the power system and offer opportunities for improved operations. In this paper, the root mean-square-error model evaluation indicator was used to compare the back propagation (BP) neural network algorithm and the traditional forecasting algorithm. The evaluation was performed on the randomly selected five household power datasets. The results show that, by comparing the long short-term memory network (LSTM) model with the BP neural network model, it was determined that the average prediction impact increases by about 25.7% under stable power demand. The short-term power prediction model of the BP neural network has the average error values more than two times lower than the traditional prediction model. It was shown that the use of the BP neural network algorithm and blockchain could increase the accuracy of short-term power demand forecasting, allowing the neural network-based algorithm to be implemented and taken into account in the research on short-term power demand forecasting

Forecasting airport passenger traffic: the case of Hong Kong International Airport

Hong Kong International Airport is one of the main gateways to Mainland China and the major aviation hub in Asia. An accurate airport traffic demand forecast allows for short and long-term planning and decision making regarding airport facilities and flight networks. This paper employs the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology to build and estimate the univariate seasonal ARIMA model and the ARIMX model with explanatory variables for forecasting airport passenger traffic for Hong Kong, and projecting its future growth trend from 2011to 2015. Both fitted models are found to have the lower Mean Absolute Percentage Error (MAPE) figures, and then the models are used to obtain ex-post forecasts with accurate forecasting results. More importantly, both ARIMA models predict a growth in future airport passenger traffic at Hong Kong